OTT Content Analytics and Predictive Machine Learning: A Structured Review of Trends, Forecasting, and Recommendation Methods

The fast growth of over-the-top (OTT) media streaming has greatly changed the way films and television shows are made and enjoyed. The growing availability of metadata from digital content allows one to use data analysis and machine learning for exploring the trends in the content, understanding its popularity, and making decisions based on facts. The paper discusses the research on OTT content analysis, prediction of its popularity, time-series forecasting, machine learning algorithms, and recommender systems. The limitations of the traditional machine learning methods, trends analysis methods and deep learning technology are reviewed and analyzed. Special attention is given to the problems related to the quality of data, feature engineering, changes in the preferences of the audience, and limitations of the usage of popularity indicators of the content instead of information related to audience viewing. A new analytical framework is proposed based on the information studied for the analysis of films and television metadata collected from The Movie Database (TMDb) between 2020 and 2025. The review also emphasizes the need to address research shortages related to the lack of uniformity in ways of assessing popularity, missing databases, issues of time sensitivity, lack of information about audience interaction, and model generalizability. The framework introduced is a starting point for systematic OTT content analysis and predictions. Its predictive power has to be demonstrated in actual studies rather than assumed off the bat