Recommender Systems (RS) play an integral role in enhancing user experiences by providing personalized item suggestions. This survey reviews progress in RS from 2017 to 2024, connecting theoretical advances with practical applications. It traces the development from traditional techniques (content-based and collaborative filtering) to advanced methods involving deep learning, graph-based models, reinforcement learning, and large language models. The work discusses specialized systems such as context-aware, review-based, and fairness-aware RS, and addresses challenges across e-commerce, healthcare, and finance, emphasizing scalable, real-time, and trustworthy solutions.