With the ever-increasing dependence on digital services, ensuring the security of user accounts has become a paramount concern. Traditional authentication methods, such as passwords and PINs, have demonstrated vulnerabilities to various attacks. Keystroke dynamics, a behavioral biometric, offers a promising solution for adaptive authentication by analyzing typing patterns unique to everyone. This project explores the implementation of keystroke dynamics in adaptive authentication systems using machine learning algorithms. The primary objective is to create a robust, secure, and user-friendly authentication mechanism that continuously adapts to the changing typing behavior of users while maintaining a high level of accuracy. The proposed system employs a diverse dataset collected from users performing various typing tasks to train machine learning models. Features such as keystroke latency, flight time, and typing rhythm are extracted and used as inputs to the algorithms. Several popular machines learning techniques, including support vector machines, neural networks, and random forests, are employed to build classification models capable of distinguishing between legitimate users and unauthorized intruders. This project advocates for the adoption of keystroke dynamics in adaptive authentication systems, utilizing machine learning algorithms to create a secure and user-friendly experience. By combining behavioral biometrics with cutting-edge technology, the proposed approach offers a robust defense against unauthorized access, paving the way for more secure and convenient authentication methods in the digital era.