With the rapid growth of social media platforms, Twitter has emerged as a significant source of real-time data reflecting public opinion, sentiment, and trends across various domains. The vast volume of tweets generated daily presents both opportunities and challenges in extracting meaningful insights. This paper explores the application of machine learning (ML) techniques to analyze Twitter data, enabling the extraction of valuable insights from the noise of social media content. Specifically, we examine how machine learning models, including supervised learning, unsupervised learning, and natural language processing (NLP) techniques, can be leveraged for tasks such as sentiment analysis, topic modeling, and trend prediction.