Personalized RSs are more important than they have ever been since the rise of the digital world, whereby the internet is too packed with information, leading to information overload. These systems would not be able to function without machine learning techniques that allow them to track and analyze user preferences to provide personalized recommendations. In this paper, we conducted a comprehensive survey of personalized recommended systems (RSs), with particular emphasis on how deep learning techniques, such as neural collaborative filtering, recurrent neural networks, and attention mechanisms, improve the quality of these systems. We also discuss other popular recommendation algorithms, such as content-based filtering, collaborative filtering, and hybrid approaches, which form the basis of modern RSs. This review outlines important features, such as user-centric recommendation algorithms, and describes the difficulties in their implementation. In addition, we outline several possible challenges for future work in this domain. The unique contribution of this paper lies in its focused analysis of deep learning-based recommendation techniques, offering a novel perspective on their comparative effectiveness and potential for future enhancements.