This book offers a comprehensive exploration of Content-Based Image Retrieval (CBIR), a technique that overcomes the limitations of traditional text-based search by leveraging intrinsic visual features such as color, texture, and shape. It highlights the evolution of CBIR, its core methodologies, and practical applications across diverse fields like healthcare, education, commerce, and crime prevention. The book delves into advanced technologies such as feature extraction, relevance feedback, and machine learning models, focusing on how they improve retrieval accuracy and efficiency. Additionally, it addresses significant challenges, including computational complexity, scalability, and ethical issues in implementing CBIR systems. Looking forward, the book explores future innovations in CBIR, such as personalized retrieval, multimodal search, and its integration with emerging technologies like augmented reality. It serves as an essential resource for researchers, practitioners, and innovators, emphasizing CBIR's transformative potential in a world increasingly driven by image and multimedia data.