Sentiment analysis in social media has become crucial for understanding public opinion and sentiment towards various entities, products, and events. This paper explores the application of machine learning (ML) approaches in sentiment analysis within the realm of social media, leveraging soft computing techniques. Soft computing methodologies, including fuzzy logic, neural networks, and genetic algorithms, offer flexible and adaptive frameworks for handling the inherent uncertainties and complexities present in social media data. This paper provides an overview of the key ML algorithms used in sentiment analysis, discusses the challenges specific to social media data, and reviews recent advancements and trends in the field. Additionally, it highlights the integration of soft computing techniques with ML models to enhance the accuracy and robustness of sentiment analysis systems.