The rapid expansion of the Internet has fostered remarkable connectivity and innovation but has also created fertile ground for escalating cybercrimes, including data breaches, phishing, ransomware, and distributed denial of service (DDoS) attacks. These crimes pose severe threats to individuals, businesses, and governments worldwide. This study examines effective strategies for combating global Internet crimes through an integrated approach that combines traditional cybersecurity methods with artificial intelligence (AI) and machine learning (ML) technologies. A comprehensive review of existing cybersecurity frameworks and an experimental assessment of proposed strategies were conducted using simulated and real-world attack scenarios. The results demonstrate that integrating AI and ML significantly enhances the detection, prediction, and mitigation of cyber threats, improving system resilience and threat response time. The study highlights the necessity of adopting proactive, technology-driven approaches to digital protection, emphasizing system hardening, threat intelligence, and user education as essential pillars of global cybersecurity. Ultimately, this research contributes to strengthening cybersecurity frameworks and provides a foundation for developing adaptive strategies to safeguard the digital ecosystem.