This research presents a novel approach to bot detection in web applications using behavioral biometrics and machine learning. Our system leverages a Flask based web application with a registration form as a testbed to distinguish between human and automated users. The implementation collects multidimensional behavioral data including mouse movements, typing patterns, form fill speed, and browser fingerprinting to build a comprehensive user profile. Two machine learning models, Random Forest and XGBoost, are dynamically compared for performance, with the superior model being automatically selected for deployment. The system incorporates a honeypot field as a simple yet effective first pass filter and implements progressive model learning through a database backed training pipeline that continually improves detection accuracy. Key innovations include the real time behavioral analysis during form completion, automated weekly model retraining, and an administrative interface that allows for manual labeling of edge cases to enhance the training dataset. Our approach achieves high detection accuracy while maintaining a low false positive rate, effectively balancing security with user experience. This research demonstrates that integrating behavioral biometrics with adaptive machine learning provides a robust defense against increasingly sophisticated bot attacks without requiring traditional CAPTCHA challenges that often degrade user experience.