These days, consumers may make well-informed automated decisions with the aid of recommendation algorithms. These systems assist the users in the identification of useful information from a wealth of information available. As to movie recommendations, they are provided considering users’ similarities (Collaborative Filtering) or focusing on a particular user’s characteristics (Content-Based Filtering). The purpose of this research is to reduce the limits of both collaborative and content-based filtering by adopting a hybrid approach to construct a more efficient recommendation system. In this work, to develop a reliable movie recommendation system, enhanced by Explainable AI (XAI) techniques to improve transparency and user trust, performed comprehensive exploratory data analysis (EDA) using the MovieLens 1 M dataset to investigate user–movie interactions and patterns. The movie recommendation hybrid model that we have used in this study has been developed by performing the dot product of the user and movie embeddings while using XAI techniques, namely LIME and SHAP, to make the recommended results more credible and reliable. The assessment of the model has been carried out with the help of precision, recall, F1-score indicators, and RMSE and MSE to quantify classification accuracy and the number of prediction errors. The results indicated that the hybrid model surpassed the individual CF and CB methods, with an accuracy of 73.35% and an RMSE of 0.2229. Manhattan, Euclidean, and Jaccard were employed to introduce user–movie similarity with Manhattan resulting in the highest similarity score of 8.0164, Euclidean with 1.3942 and Jaccard with 1. These similarity ratings have thus been instrumental in improving suggestion accuracy. Moreover, XAI improved the model’s interpretability, which means that the users realized why exactly such a suggestion was made–and understanding such nuances is crucial to increasing the usage rate.