Recommendation systems play a crucial role in personalized content delivery across various domains such as e-commerce, streaming platforms, and healthcare. This survey presents a comprehensive analysis of recommendation frameworks, emphasizing their architectures, methodologies, challenges, and future directions. The provided framework integrates hybrid models, deep learning, collaborative filtering, and content-based filtering, processed through a multi-layered architecture. The data processing layer handles preprocessing, feature extraction, and data collection, while the model selection layer chooses an appropriate recommendation technique. The recommendation engine ranks and scores predictions before delivering final recommendations. A critical component is the user feedback & continuous learning module, incorporating explicit and implicit feedback to dynamically update the model. Challenges such as scalability, data sparsity, and real-time adaptation are explored, along with emerging advancements like knowledge graphs and reinforcement learning. The paper highlights future research opportunities to enhance recommendation accuracy and user experience.