In an era where entertainment content is becoming more digitally connected, tailored movie suggestions become more crucial for raising user pleasure and engagement. This study uses Enhanced Self-organizing Maps (SOM) to provide a new method for making movie choices easier. Unsupervised neural network architectures, or SOMs for short, are useful in recommendation systems because they are good at identifying complex data patterns. The approach presented in this paper begins with the collection of user-movie interaction data, which includes user reviews and movie attributes. To maintain uniformity, this data is standardized before being used to train the model. Because of its flexible learning rate and neighborhood function, the Enhanced SOM is effective at identifying subtle patterns in data. Customized movie recommendations are created using the Enhanced SOM’s ability to recognize similar users and films. The integration of hybrid filtering techniques by the framework enhances the quality of suggestions. Collaborative filtering algorithms make advantage of user-item interactions, whereas content-based filtering makes use of movie properties like genres and descriptions. These approaches result in suggestions that combine several filtering techniques in a synergistic way. The effectiveness of the suggested solution is assessed rigorously by comparing the accuracy of the suggestions and user satisfaction to predetermined criteria. Extensive testing using real-world datasets confirms the effectiveness of the Enhanced SOM-based movie recommendation method. The system includes possibilities for parameter change, grid size variations, and neighborhood function alterations to further improve recommendation quality. All of these aspects together highlight how effective the suggested method is in providing tailored movie recommendations. When combined with hybrid filtering techniques, the implementation of Enhanced SOMs represents a dependable model for content platforms looking to improve user experiences by providing accurate movie recommendations along with scalability and flexibility.