The rapid expansion of products, services, and choices on digital platforms has made it increasingly difficult for users to identify the most relevant options. Recommender systems have since then been one of the major ways in addressing this challenge by offering personalized recommendations to users for making choices from such a large pool of options. These began in the 1990s and became fundamental on a wide range of online platforms, including e-commerce, streaming services, healthcare, and education, greatly enhancing user satisfaction. However, traditional recommender systems often face difficulties in adapting to changing user preferences, necessitating the development of adaptive systems that can continually adjust to changes in user behavior. This paper presents an in-depth review of adaptive personalized recommender systems, highlighting their ability to evolve, as well as their strengths, weaknesses, and diverse applications. Through an analysis of recent research, this work provides an exhaustive examination of adaptation strategies, key metrics, commonly used datasets, and various application domains, while also addressing the systems’ benefits, limitations, and potential areas for future research.