Scraping Google Images presents unique challenges due to its dynamic elements such as CSS selectors and Base64 encoding, which require a more puzzle-solving approach than typical HTML scraping. The process involves using Python and Selenium to navigate and extract images, which are embedded in custom HTML tags and encoded in Base64 strings. These strings contain the binary data of the images, allowing them to be saved locally after decoding. A step-by-step guide is provided, including setting up a Python scraper to automate the process, utilizing tools like ChromeDriver for browser automation, and handling images through a combination of string manipulation and Base64 decoding. Additionally, an alternative method using Bright Data's SERP API is suggested for a more streamlined approach, offering scalability and metadata retrieval without the need for Selenium, thus simplifying the extraction process for large-scale operations.