SmartRecoNet is a deep learning-based hybrid framework designed to deliver intelligent, personalized smartphone recommendations by leveraging diverse data sources and user-specific factors. The system integrates user reviews, smartphone specifications, real-time trends, individual preferences, budget constraints, and demographic information to generate ranked suggestions tailored to each user. By combining the strengths of multiple deep learning models, SmartRecoNet delivers recommendations that are not only accurate but also contextually relevant and timely. The framework incorporates sentiment understanding through advanced natural language processing, trend forecasting using time-series modeling, feature encoding for device specifications, and personalized preference profiling using collaborative filtering. A hybrid recommendation engine synthesizes these components to provide a comprehensive and user-aware ranking of smartphones. One of the key strengths of SmartRecoNet lies in its adaptability. It continuously learns from real-time user feedback such as clicks and purchases, ensuring that the recommendation quality evolves with changing user behavior. Additionally, the system incorporates periodic updates of its core models to remain aligned with current market dynamics and consumer sentiment. A/B testing is employed during deployment to validate performance and user satisfaction. By unifying sentiment, behavior, preferences, and specifications, SmartRecoNet offers a scalable and intelligent solution for users seeking the best smartphones aligned with their personal needs and market trends. The system demonstrates clear advantages in terms of personalization, accuracy, diversity, and responsiveness, making it a valuable tool for enhancing the smartphone shopping experience.