The ever-increasing growth of Big Data brings with it both remarkable opportunities and substantial security considerations. Machine Learning, an exciting subfield of artificial intelligence, can be an extremely useful asset in boosting the security of Big Data systems. This paper explores the application of Machine Learning in the realm of Big Data security, delving into topics such as anomaly detection, behavior analysis, risk assessment and priority rank. Several real-world examples have been identified to outline the possible applications and benefits of Machine Learning in terms of Big Data security. To be sure, the paper also evaluates the constraints and goals faced by Machine Learning for Big Data security, along with promising suggestions for future research. All in all, this article brings to light the powerful effect that Machine Learning can have on the security of Big Data systems and its capacity to redefine the field of Big Data security.