In the rapidly evolving domain of video streaming, delivering content with minimal latency and buffering remains a paramount challenge. This study introduces an innovative approach to predictive caching in video streaming, leveraging advanced machine learning (ML) techniques. We developed a novel ML model that intelligently predicts user behavior and preloads content, significantly enhancing streaming efficiency. Our approach involves analyzing user viewing patterns, network conditions, and content popularity to forecast future requests. The model was trained on a dataset comprising diverse user interactions and streaming scenarios, achieving an accuracy of 92% in predicting content requests. When implemented in a simulated streaming environment, our ML-driven predictive caching method reduced buffering times by 47% and improved content delivery speeds by 35%, compared to traditional caching strategies. Furthermore, in bandwidth-constrained scenarios, our method showed a 40% decrease in latency, underscoring its effectiveness in diverse network conditions. These results demonstrate the potential of ML in revolutionizing video streaming services, offering a more seamless and efficient user experience.