This paperconcentrates on the early prediction and classification of Distributed Denial of Service (DDoS) assaults using ML approaches to enhance network security and mitigate the impact of cyber threats on digital infrastructures. In the battle against distributed denial of service (DDoS) attacks, which disrupt network services by overwhelming systems with unwanted traffic in large quantities, early detection and accurate classification are essential to ensure service availability and safeguard sensitive data. The research employs robust machine learning algorithms to evaluate network traffic patterns, identify suspicious activities, and differentiate between secure and unsafe actions in real time. In order to achieve more accurate predictions with fewer false alarms, it is necessary to implement a sequence of procedures that encompasses data collection, preprocessing, feature extraction, feature selection, model training, and performance evaluation. The proposed method utilizes sophisticated analytical tools and supervised learning models to rapidly identify attack patterns before they cause significant harm. The project’s objective is to create an intrusion detection system that is scalable, efficient, and dependable in order to enhance cybersecurity defenses and facilitate proactive threat management in contemporary network environments.