The rapid digitalization of modern economies has expanded the attack surface of critical systems, exposing organizations and governments to increasingly sophisticated cyber threats. Traditional rule-based defense mechanisms and static security architectures are proving insufficient in countering advanced persistent threats, zero-day exploits, and highly adaptive adversaries. Artificial intelligence (AI), particularly deep neural networks (DNNs), has emerged as a cornerstone for enhancing cybersecurity through automated intrusion detection, anomaly detection, and real-time threat response. However, the vulnerability of these models to adversarial attacks presents a critical weakness that adversaries can exploit. Adversarial machine learning (AML) has become a focal area in strengthening DNNs against evasion attacks, where malicious inputs are designed to bypass detection, data poisoning, where training sets are corrupted, and model-inference attacks, which extract sensitive information from trained models. This research emphasizes the integration of adversarial training, robust optimization, and detection of adversarial examples to improve the resilience of cybersecurity systems. By leveraging explainable AI and graph-based learning mechanisms, we propose defense strategies that provide transparency, adaptability, and robustness across dynamic cyber environments. The study highlights the importance of balancing predictive performance with robustness to ensure practical deployment in high-stakes domains such as finance, defense, and healthcare. We also discuss emerging challenges, including computational overheads, adversarial transferability across models, and the difficulty of benchmarking robustness in real-world scenarios. Ultimately, adversarial machine learning offers a transformative pathway toward developing resilient, trustworthy cybersecurity infrastructures capable of defending against evolving attack vectors while safeguarding data integrity, confidentiality, and system reliability.