<p>The emergence of smart cities has led to an unprecedented increase in the volume of data generated by sensors, social media, and mobile devices, among other sources. Recommender systems in this environment are employed to personalize user experiences, enhancing engagement and overall quality of life. Collaborative filtering (CF) is a widely employed approach in recommender systems that has been demonstrated to accurately predict user preferences based on the behavior of similar users. Recently, deep learning methods have been used to improve the performance of CF-based recommender systems by capitalizing on the capacity of deep neural networks to recognize complex data patterns. However, there is a lack of synthesis of the state of the art in the literature on the application of deep collaborative filtering (DCF) in the context of smart cities. Therefore, it is essential to conduct a systematic review to identify and analyze the existing research on DCF-based recommender systems in smart cities in order to provide a comprehensive overview of the current state of the art and to identify research gaps and challenges. This paper will provide valuable insights for researchers, practitioners, and decision makers interested in designing and implementing effective recommender systems in smart cities, with the goal of enhancing the overall quality of life and well-being of citizens.</p>