This chapter described the details about the implementation of machine learning in oil spill. Besides that, this chapter explores the application of Support Vector Machines (SVMs) as an advanced machine learning technique, focusing in oil spill fingerprinting, and a vital component on environmental forensics. The aim of this chapter is to accurately classify and predict the origin of oil spills by analyzing complex chemical compositions and environmental parameters. The key advantages of the SVM approach are high predictive accuracy, fast computational performance, and also reliable separation of complex chemical profiles. Through kernel-based mapping and margin maximization, the SVM model demonstrates its potential in identifying oil spill sources, supporting legal accountability, and enhancing mitigation planning. The chapter concludes that SVMs provide a scientifically sound and computationally efficient methodology for tackling real-world environmental classification challenges.