This comprehensive review explores sentiment analysis techniques within the domains of movie reviews and product feedback. It begins by examining foundational methodologies, including lexicon-based approaches and machine learning techniques. The paper delves into the transformative impact of deep learning methodologies, highlighting the effectiveness of recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models such as BERT and GPT in capturing nuanced semantics and contextual dependencies. Additionally, ensemble methods and meta-learning strategies for improved sentiment classification are discussed. Addressing challenges specific to these domains, such as data scarcity and sarcasm detection, the review outlines future research directions. Through meticulous analysis and synthesis, it provides a comprehensive guide for researchers and practitioners navigating sentiment analysis within movie reviews and product feedback domains.