Reviews found online are a great resource for both customers and companies. However, processing such huge data efficiently is difficult, thus we need a machine learning system. Machine learning can analyse and interpret the massive volume of data provided by internet reviews. Sentiment analysis methodologies are examined in this chapter on machine learning and internet reviews. Then, it assesses sentiment analysis's essential components, applications, and challenges. Machine learning methods have proven effective in helping individuals, businesses, and governments extract useful insights from the vast amounts of unstructured data found in online evaluations. More efficient methods of addressing the issue of fraudulent and disorganised reviews must be actively considered. The utilisation of secondary data allowed for the successful completion of this book chapter. Machine learning has revolutionized the way we analyze and make sense of online reviews. It gives companies the ability to glean insightful information from this abundance of consumer feedback, ranging from sentiment analysis and aspect-based sentiment analysis to creating strong recommender systems and identifying fraudulent reviews. As the volume of online reviews continues to grow, the role of machine learning in this domain is only expected to become more significant, providing consumers with better information and businesses with more effective tools for improving their products and services.