The rapid digital transformation across industries has significantly increased the prevalence and sophistication of cybercrime, posing severe threats to individuals, organizations, and governments. Traditional security mechanisms are often inadequate in detecting and mitigating such evolving threats. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) techniques have emerged as powerful tools for enhancing cyber defense systems. This paper presents a comprehensive study on AI-driven approaches for cybercrime detection and prevention, focusing on machine learning models such as Support Vector Machines, Random Forest, Deep Neural Networks, and Hybrid Architectures. AI-powered systems use techniques like anomaly detection, pattern recognition, and predictive analytics to spot malicious activities in real time. This helps them cut down on false alarms and quickly adapt to new types of threats. Furthermore, this research highlights the role of explainable AI (XAI) and federated learning in improving trust, privacy, and scalability of cyber defense frameworks. The study concludes that AI- driven solutions are not only effective in preventing cybercrime but also essential for building proactive, resilient, and adaptive security infrastructures in the digital era.
Cybercrime, Artificial Intelligence, Machine Learning, Deep Learning, Intrusion Detection, Anomaly Detection, Cybersecurity, Predictive Analytics, Explainable AI, Federated Learning.