How to Scrape Google Maps Data (Leads, Reviews & Listings) in 2026

“Can you get me every dentist in Texas with a phone number and a website?” is one of the most common requests I get, and Google Maps is almost always where that data lives. Business name, address, phone, website, category, rating, review count, opening hours — it’s a clean, structured lead list hiding in plain sight.

Here’s how scraping Google Maps actually works in 2026, the three routes you can take, and the parts that trip people up.

First, is this legal?

Scraping publicly visible business listings — the same information any customer sees without logging in — is broadly considered fair game, and courts have generally sided with scrapers collecting public data. What you want to avoid is: data behind a login, personal data on private individuals, and hammering the service so hard you degrade it. Collect public business info, rate-limit yourself, and use it responsibly. That’s the line I stay well inside of, and you should too.

The three ways to get Google Maps data

1. The official Places API — clean but capped

Google’s own Places API returns listing data as tidy JSON. It’s the “correct” route and the data quality is excellent. The catches: it’s billed per request, results per query are capped, and some fields (like full review text or email) simply aren’t exposed. For a few hundred lookups where you already know the places, it’s great. For “every restaurant in five cities,” the bill and the caps get painful fast.

2. Scraping the Maps front-end — flexible but fiddly

This is what most people actually mean. You drive the Maps search UI, scroll the results panel to load more listings, and extract each card. Because Maps is a heavy JavaScript app, plain requests won’t cut it — you need a headless browser (Playwright/Puppeteer) to render and scroll:

from playwright.sync_api import sync_playwright

def scrape_maps(query):
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()
        page.goto(f"https://www.google.com/maps/search/{query}")
        # scroll the results panel until no new cards load
        panel = page.locator('div[role="feed"]')
        for _ in range(20):
            panel.evaluate("el => el.scrollTo(0, el.scrollHeight)")
            page.wait_for_timeout(1800)
        cards = page.locator('div[role="feed"] > div > div[jsaction]')
        # ...extract name, rating, link from each card, then open for phone/website
        browser.close()

The real work isn’t the scroll — it’s opening each listing to grab phone and website, handling the layout changing between listing types, deduping, and dealing with Google throttling you after a few hundred requests from one IP. That last one is where proxies come in.

3. Done-for-you — skip straight to the spreadsheet

If you just want the list, the fastest path is to have someone hand you a clean, deduplicated CSV or Google Sheet. (More on that at the bottom — it’s most of what we do.)

Not getting blocked

Google is very good at spotting automation. The three things that keep a Maps scraper alive:

1. Rotate residential IPs. After a few hundred requests from one address, Google starts serving CAPTCHAs. Spreading requests across residential proxies is the single biggest fix — I compared the providers I actually use in Best Residential Proxies for Web Scraping.
2. Go slow and human. Randomized delays between scrolls and clicks, not a machine-gun loop. See the delay/jitter pattern in How to Scrape a Website Without Getting Blocked.
3. Grab the underlying data, not screenshots. Maps fetches listing data from internal endpoints as you scroll — reading those responses is far cheaper and more reliable than parsing rendered HTML.

The fields worth collecting

For lead lists, the high-value columns are: business name, category, full address, phone, website, rating, review count, and hours. Email usually isn’t on the listing — you get it by visiting the business’s website and extracting the contact address, which is a second scraping step worth building in from the start.

Getting it somewhere useful

A pile of JSON isn’t a lead list. The last mile — deduplicating (Maps repeats listings across searches), normalizing phone formats, filtering out the places with no website or under a rating threshold, and pushing it into a Google Sheet, Airtable, or your CRM — is what turns scraped data into something sales can actually use. Build that step in; don’t bolt it on later.

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If you’d rather just receive the finished, deduplicated lead list — or want a Google Maps scraper built to run on a schedule and drop fresh leads into your CRM — send us the details or message me on Fiverr. Pulling clean business data at scale is exactly what we do.