srjecsSRJECSSRJECS2788-940810.47310/srjms.2025.v05i01.016Network-Based Movie Quality Prediction and Recommendation System Using Hybrid Machine Learning TechniquesThe current streaming platform era requires precise high-quality movie recommendations to boost user satisfaction and platform engagement. Traditional filtering which utilizes word search year of production, number of views, or movie rating may not provide best user experience. Thus, in this research we introduce a Movie Quality Recommendation System which uses machine learning methods to suggest movies and forecast their quality through analysis of genre and popularity alongside ratings and textual descriptions. Our system uses content-based filtering and collaborative filtering and hybrid recommendation models together with regression-based quality prediction to deliver precise personalized movie suggestions. The architecture adopts a network-based perspective through modeling user–item interactions and content similarities as interconnected graphs which enables structured reasoning across relationships. The system shows how machine learning enhances movie recommendation systems through its implementation of Python and Flask together with Scikit-learn and TensorFlow.