The Image-Based Search Engine is a revolutionary approach to information retrieval that leverages image processing, face detection, and web scraping techniques to provide users with relevant URLs based on uploaded images. With the exponential growth of digital content, traditional text-based search engines struggle to effectively process and understand visual data. This project addresses this limitation by analyzing the content of input images, detecting faces using advanced algorithms like Dual Shot Face Detector (DSFD), and retrieving URLs related to the image. The system seamlessly integrates face detection and web scraping, presenting users with a user-friendly web page where they can upload an image and obtain a curated list of URLs. By using the facial region (if detected) or the entire image (if no face is present), the system ensures comprehensive link retrieval based on visual cues. The Image-Based Search Engine holds significant potential across various domains such as e-commerce, social media analysis, and research/education, enabling users to find relevant information, make purchases, identify influencers, and locate scholarly resources. This paper provides an in-depth description of the engine’s architecture, methodologies, and the integration of face detection and web scraping techniques. Additionally, it discusses the potential applications, benefits, and limitations of this intelligent system, highlighting its ability to enhance the efficiency and effectiveness of information retrieval in the visually-driven digital landscape. The Image-Based Search Engine represents a significant advancement in the field of information retrieval, harnessing the power of computer vision and web scraping to offer users a more intuitive and visually oriented approach to finding online content.