This study explores the application of Neural Collaborative Filtering (NCF) in the domain of e-commerce to enhance conversion rates and customer satisfaction through personalized product recommendations. Leveraging a dataset collected from a prominent online retailer, the NCF model is trained to predict user preferences based on their interactions with various items. Three different configurations of the NCF model are experimented with, each employing varying layer architectures, and their performance is evaluated using the Hit Rate (HR) metric. Results indicate that the model with the most sophisticated layer configuration achieves the highest HR, suggesting improved recommendation accuracy. The ability of the NCF model to capture complex user-item interactions enables it to provide personalized recommendations, thereby enhancing user engagement and satisfaction. This study highlights the potential of deep learning techniques, specifically NCF, in optimizing e-commerce recommendation systems. Future research could extend this work by exploring real-time recommendation adaptations and cross-domain applications to further validate NCF’s potential in diverse e-commerce environments.