Find Meesho Trending Products for India Dropshipping (2026)

Thirdwatch's Meesho Scraper returns Meesho catalog data tuned for trending-product discovery — popularity ranks, newest-arrival slots, rating velocity, discount depth, and category coverage. Built for India dropshippers, D2C founders researching breakout SKUs, and sourcing analysts identifying products to import from AliExpress or Indian wholesalers.

Why scrape Meesho for India dropshipping discovery

Meesho is the fastest-moving large product catalog in India. According to Meesho's pre-IPO disclosures and the Bain India How India Shops 2025 report, the platform adds millions of new SKUs every month, most of them reseller-supplied from a base of small manufacturers in Surat, Tirupur, Ludhiana, and Delhi NCR. Trending products surface on Meesho first because resellers experiment cheaply and abandon non-performers quickly — the platform is a real-time signal of what India's Tier 2/3 consumer is currently buying.

For an India dropshipper or D2C founder, this matters. A new tea-infuser concept hitting 5,000 reviews in 30 days on Meesho is a sourcing tell. A kids ethnic-wear pattern crossing 10,000 reviews ahead of Diwali is a category breakout. A men's casual-shirt design clustering with multiple resellers within weeks of first appearance signals a copycat-worthy hit. Static category research misses all of this — the signal is in the velocity and the cross-section, not the snapshot.

How does this compare to alternatives?

Three operational paths to Meesho trending-product data:

Most retail-intelligence vendors focus on Flipkart and Amazon India and treat Meesho as a long-tail afterthought. The Meesho Scraper actor page gives you raw structured rows for your warehouse, which is what trending-product velocity analysis actually requires — week-over-week diffs, not a vendor-curated dashboard.

How to find trending Meesho products in 4 steps

Step 1: How do I authenticate against Apify?

Get a free Apify API token at apify.com, then export it.

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull this week's top products by category?

sortBy=popularity returns Meesho's own ranking of established hits. Pull deep enough (200-500 per category) to capture long-tail breakouts.

import os, requests, datetime, json, pathlib

ACTOR = "thirdwatch~meesho-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

CATEGORIES = [
    "women-ethnic", "women-western", "men",
    "kids", "home-kitchen", "beauty-health",
    "jewellery-accessories", "bags-footwear",
]

today = datetime.date.today().isoformat()
pathlib.Path("snapshots").mkdir(exist_ok=True)
all_records = []

for cat in CATEGORIES:
    resp = requests.post(
        f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
        params={"token": TOKEN},
        json={
            "queries": [],
            "category": cat,
            "sortBy": "popularity",
            "maxResults": 300,
        },
        timeout=900,
    )
    rows = resp.json()
    for r in rows:
        r["_pulled_at"] = today
        r["_category_pulled"] = cat
    all_records.extend(rows)
    print(f"{cat}: {len(rows)} products")

pathlib.Path(f"snapshots/meesho-popular-{today}.json").write_text(json.dumps(all_records))

This is the weekly baseline — ~2,400 popularity-ranked SKUs across 8 categories.

Step 3: How do I find breakout SKUs (new and already popular)?

The strongest trending-product signal is a recently listed product that has already accumulated reviews. sortBy=newest plus a rating_count filter isolates this.

import pandas as pd

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={
        "queries": ["kurti", "saree", "kids dress", "kitchen organizer",
                    "wireless earphones", "trolley bag"],
        "sortBy": "newest",
        "maxResults": 200,
    },
    timeout=900,
)
df = pd.DataFrame(resp.json())

breakouts = (df[df.rating_count.fillna(0) >= 500]
             .sort_values("rating_count", ascending=False))
print(breakouts[["product_name", "brand", "price", "original_price",
                 "discount_percent", "rating", "rating_count",
                 "category", "url"]].head(20))

A newly listed product with 500+ reviews is almost always a breakout — Meesho's newest sort is recency-ordered, so anything in this slice has crossed the threshold fast. Lower the cutoff to 200 for niche categories with smaller review volumes.

Step 4: How do I detect rising stars week-over-week?

Compare this week's rating_count against last week's by URL to surface fast-rising SKUs.

import glob, pathlib

frames = []
for f in sorted(glob.glob("snapshots/meesho-popular-*.json")):
    date = pathlib.Path(f).stem.replace("meesho-popular-", "")
    for j in json.loads(pathlib.Path(f).read_text()):
        frames.append({
            "date": date, "url": j.get("url"),
            "product_name": j.get("product_name"),
            "category": j.get("category"),
            "price": j.get("price"),
            "rating": j.get("rating"),
            "rating_count": j.get("rating_count") or 0,
            "discount_pct": j.get("discount_percent"),
        })

ts = pd.DataFrame(frames).dropna(subset=["url"])
ts["date"] = pd.to_datetime(ts["date"])
weeks = sorted(ts.date.unique())[-2:]
last = ts[ts.date == weeks[0]].set_index("url")
this = ts[ts.date == weeks[1]].copy()
this["prev_reviews"] = this.url.map(last.rating_count)
this["delta_reviews"] = this.rating_count - this.prev_reviews.fillna(0)
this["pct_growth"] = (this.delta_reviews /
                      this.prev_reviews.replace(0, pd.NA)) * 100

rising = (this[(this.delta_reviews >= 200) & (this.rating >= 4.0)]
          .sort_values("delta_reviews", ascending=False))
print(rising[["product_name", "category", "price", "rating",
              "prev_reviews", "rating_count", "delta_reviews",
              "discount_pct", "url"]].head(15))

200+ new reviews in a week on a 4.0+ rated product is the canonical "rising star" filter for India social commerce. Push this list to your sourcing team or your AliExpress-import-research queue.

Sample output

A single record. Trending-research analysis usually involves 1,500-3,000 records per weekly pull.

[
  {
    "product_name": "Women's Floral Print Anarkali Kurta with Dupatta",
    "brand": null,
    "price": 599,
    "original_price": 1999,
    "discount_percent": 70,
    "rating": 4.2,
    "rating_count": 15842,
    "image": "https://images.meesho.com/images/products/...",
    "url": "https://www.meesho.com/womens-floral-anarkali-with-dupatta/p/qrs456",
    "category": "women-ethnic"
  },
  {
    "product_name": "Stainless Steel Modular Kitchen Storage Set",
    "brand": null,
    "price": 449,
    "original_price": 1499,
    "discount_percent": 70,
    "rating": 4.0,
    "rating_count": 6710,
    "image": "https://images.meesho.com/images/products/...",
    "url": "https://www.meesho.com/kitchen-storage-set/p/uvw789",
    "category": "home-kitchen"
  }
]

For trending-product workflows the load-bearing fields are rating_count (growth velocity), discount_percent (markdown depth signals reseller saturation), and url (cross-snapshot key). brand: null is normal — most Meesho catalog inventory is unbranded reseller-supplied SKUs.

Common pitfalls

Four patterns surface in production trending-product pipelines on Meesho. Survivorship bias in popularity sort — popularity-ranked products are by definition already big; for breakout discovery you must combine with the newest-sort + rating_count cross-section described above. Review-velocity noise — review counts can spike from a single influencer mention; cross-reference rating value (stay above 4.0) and discount_percent (avoid the rare 90%+ clearance dumps). Category boundary fuzziness — Meesho's category assignment is reseller-supplied and noisy; for analytics dedupe by product_name n-gram clusters within a category rather than trusting raw category strings. Seasonal cycling — kids and ethnic wear spike around school terms and festivals (Diwali, Eid, Onam, Raksha Bandhan); de-seasonalize using a 4-week trailing baseline before flagging breakouts.

Thirdwatch's actor handles Meesho's anti-bot defenses (the platform aggressively blocks raw HTTP clients) and tracks site changes so your weekly pipeline keeps working. You write the keyword and category list; the actor returns clean structured rows ready for warehouse analytics. Pair this with our AliExpress Scraper to research the sourcing side of every trending SKU you find on Meesho — that price gap is your working margin.

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