This dissertation presents a comprehensive analysis of recommender systems, focusing on the integration of visual data to enhance recommendation accuracy and reliability. Chapter One provides a General introduction to recommender systems, emphasizing their importance in our nowadays technologies. Chapter Two explores the state-of-the-art in recommender systems, discussing various methodologies and advancements, with a particular focus on the incorporation of visual data. The chapter highlights the transformative potential of visual data integration in shaping personalized recommendation experiences across different platforms and domains and then we conclude the study by synthesizing the the summarizing of the State-of-the-Art and the challenges associated with visual data integration.