The distributed denial-of-service (DDoS) attacks present a formidable obstacle to contemporary cyber security, necessitating advanced detection techniques. This project addresses this urgency by developing robust DDoS attack prediction and classification systems leveraging machine learning (ML) methodologies, including random forest, bagging, gradient boosting classifier (GBC), and extra trees. It recognizes key challenges in DDoS attack detection, such as dynamic attack landscapes and differentiating between legitimate and malicious traffic. Through meticulous ML pipeline stages, ranging from data collection to model deployment, the project systematically addresses these challenges. It employs diverse datasets, visualizes data for insight, and preprocesses data for model enhancement. Rigorous model training, evaluation, and algorithm selection ensure robust classifiers capable of accurately categorizing DDoS attacks. Moreover, the project extends to real-time model deployment in a web application, enabling proactive defense against emerging threats. By integrating ML techniques and effective deployment, this approach enhances cyber security measures, contributing significantly to safeguarding digital infrastructures. The comprehensive strategy outlined herein offers organizations proactive measures against evolving cyber threats, underscoring the advancement in cyber defense strategies.