Each of us needs entertainment to recharge our spirits and energy in this fast-paced world. Our confidence for work is restored by entertainment, and we work more ardently as a result. We can watch our favorite movies or listen to our favorite music to reenergize ourselves. Since finding chosen movies will take more and more time, which one cannot afford to waste, we can use more reliable movie recommendation algorithms to watch good movies online. In this paper, a hybrid approach that combines content-based filtering, collaborative filtering, using Support Vector Machine as a classifier, and genetic algorithm is presented in the proposed methodology. Comparative results are shown, showing that the proposed approach shows an improvement in the accuracy, quality, and scalability of the movie recommendation system than the pure approaches in three areas: accuracy, quality, and scalability. The advantages of both approaches are combined in a hybrid strategy, which also seeks to minimize their negative aspect