Recommendation systems have become indispensable in the digital world for personal content delivery to combat information explosion. Classical methods including matrix factorization, content-based filtering, and collaborative model have been built up one over another by deep learning or hybrid model. Recent approaches use Graph Neural Networks to model intricate user–item relationships, Recurrent and Convolutional Neural Networks to capture sequential spatial information, and transformer-based models (e.g., GPT-5, T5. 2) for enhanced contextual understanding. Multimodal methods (CLIP 2, Swin Transformer v3) combine textual, visual, and structural information to make accurate recommendations, and reinforcement learning-based algorithms like Proximal Policy Optimization and Deep Q-Networks can adapt to user actions. Evaluation is now moving beyond the focus on accuracy to fairness, diversity, and explanation. This review consolidates recent contributions in various domains such as e-commerce, streaming services, social media, and healthcare, and proposes future lines in the topics of hybrid algorithms privacy-preserving recommendation engines or context-aware systems which link technological progress with ethical behavior.