In this modern era, the digital landscape has continuously evolved along with increased sophisticated cyberattacks which pose a significant threat to individuals, organizations, and nations. Investigation of these advanced cyberattacks often fails due to traditional investigational methods. This research paper proposes a Digital Forensic Model for Cyber Attacks Investigation (DFMCAI) and DDoS (Distributed Denial of Service) analysis using Machine Learning to enhance the efficiency and accuracy of cyberattack investigations. DFMCAI leverages the power of machine learning techniques to identify, analyse, and attribute cyberattacks, developing a useful tool for cybersecurity experts. This research presents the conceptual framework, and methodology and highlights its potential to revolutionize digital investigations in the context of cyberattacks. The research study introduced the DFMCAI Model for DDoS analysis, which employed five machine learning algorithms to address DDoS classification. The model’s performance evaluation utilized the UNSW-NB15 dataset and found that Random Forest outperformed the other algorithms on this dataset.