In this constantly evolving field of cybersecurity, protection of sensitive information and computer systems remains the most pressing challenge. Achieving it by striking a balance between seamless access for authorized users and strong defenses against unauthorized intrusions. While traditional methods of authentication, which rely on usernames and passwords, have been dominant in this field for years, they come with significant vulnerabilities such as brute force attacks, phishing, and password theft. In response, methods of biometric authentication have become prevalent with the emergence of keystroke dynamics as a promising alternative. This study addresses the development of keystroke dynamics-based authentication methods that are enhanced using machine learning algorithms. Experimental results point to the efficiency of these models, with the CTGAN model reaching 99.9% accuracy at an EER of 0.01 and achieving accuracies of 97.0% and 97.9% for Random Forest and Histogram Gradient Boosting, respectively. All these findings illustrate the possible integration of latest technologies toward developing secure yet efficient authentication systems to guard digital resources in the increasingly connected world.