Acceptance of information-gathering behaviours, especially from the viewpoints of others, is crucial for enhancing decision-making processes. In film critiques, audience feedback offers significant insights regarding a movie’s quality and value as a time investment. The growing volume of review data requires automation for effective processing. This study introduces the creation of a sophisticated movie recommendation engine that amalgamates many data sources, such as IMDB movie reviews, Netflix ratings, YouTube trailer interactions, and Twitter discourse. User-generated input, including comments, likes, tweets, and trailer replies, is integrated to improve the recommendation process. The suggested methodology utilizes collaborative and content-based filtering, integrating users’ social influence as assessed through their Twitter activity and social characteristics. A hybrid recommendation method produces an initial array of film recommendations. Thereafter, sentiment analysis is utilized to enhance and improve the recommendations. The system employs artificial intelligence methodologies, utilizing the IMDB dataset to train and validate a BERT embedding layer alongside a Bi-Directional Long Short-Term Memory (LSTM), a Bi-Directional Gated Recurrent Unit (GRU) incorporating a self-attention mechanism, and a Convolutional Neural Network (CNN). The model attained a testing accuracy of 93.91% and an AUC of 0.9831, indicating exceptional performance in binary sentiment categorization relative to current methodologies.