The digital evidence is highly complex and amounts to large size; hence, complicating the digital forensic investigations. The contemporary computer threats require improved gadgets that are able to keep up with the dynamic changes. In the current study, the author introduces a hybrid machine learning model that combines a Support Vector Machine (SVM) and Random Forest (RF) along with Logistic Regression (LogReg) with the help of a soft voting classifier. Data imbalance, real-time processing, and legal admissibility are the critical challenges that the proposed model will handle. On the UNSW-NB15 dataset, the hybrid system scored an accuracy of 98.3 percent, precision, recall, and AUC-ROC equal to 98.1, 98.5, and 0.99, respectively, better than single classifiers. The framework can be scaled, and it is interpretable and can be applied in real-time; hence appropriate to be deployed in real-world forensics. The study will present a powerful and legally acceptable AI-powered technique of digital forensics to improve the effectiveness of cybercrime discovery and correlate them with accountability and the stability of operations.