Software piracy has become a major problem for developers and companies as software development keeps growing and changing. This paper provides a thorough overview of machine learning methods used to analyze installation metrics and user behavior patterns in order to identify and prevent software piracy. The study looks at how characteristics like usage hours, number of installations, and licensing status might predict the possibility of unlicensed consumption using algorithms like Decision Trees, Support Vector Machines, and Neural Networks. The survey assesses how well current approaches detect unlicensed software usage, with a particular emphasis on feature engineering, classification strategies, and model evaluation metrics. The study also highlights gaps in the literature, especially in areas like real-time detection, adaptive models, and interaction with software-as-a-service platforms, while identifying themes that are frequently addressed, such classification accuracy and user profiling. This initiative intends to contribute to a better secure software ecosystem, safeguard intellectual property, and offer insights into improving pirate detection systems by investigating these topics.