The rise of streaming services, personalized content recommendation is one of the critical features enhancing user engagement and retention. This paper presents a comprehensive analysis of the Netflix recommendation system, which bases its predictions on machine learning and collaborative filtering from behavioral data about the viewers’ preferences. It combines the two techniques into a hybrid approach to create personalized recommendations. It further honed the system using the technique of Singular Value Decomposition and natural language processing with enhanced accuracy for recommendations relevant to the viewer. This is realized by dynamism whereby it is possible to learn through the models that the viewers’ tastes change over time by feature engineering and techniques based on deep learning. Hence, there is alignment with actual viewer preferences at the more precise level. This research demonstrates and depicts how these methodologies efficiently work toward improving viewer satisfaction and therefore significantly contribute toward the competitive advantage of a company such as Netflix, within the very competitive streaming market. The study provides prime ideas and guidelines for progress into future advancement regarding the recommendation system in streaming platforms.