The rapid integration of artificial intelligence (AI) into critical systems has amplified concerns about its safety and security. Machine Learning (ML) and Reinforcement Learning (RL), while enabling advanced decision-making capabilities, are susceptible to a range of threats, including adversarial attacks, data poisoning, and model exploitation. These vulnerabilities not only compromise system integrity but also pose significant risks in applications such as healthcare, finance, and autonomous systems. This paper explores a comprehensive framework for ensuring the security of ML and RL models, emphasizing proactive and reactive strategies. We begin by identifying common attack vectors in ML and RL, illustrating real-world examples of security breaches. A taxonomy of these threats is presented, categorizing them based on their origin, impact, and detectability. Building on this, the paper highlights cutting-edge techniques for securing AI models, including robust model architectures, adversarial training, differential privacy, and federated learning. The role of explainable AI (XAI) in uncovering potential vulnerabilities is also examined, alongside mechanisms for enhancing model interpretability. Furthermore, the unique challenges posed by RL systems, such as the exploitation of reward mechanisms and policy manipulation, are discussed. Solutions tailored to RL, including dynamic reward shaping and environment-aware defenses, are proposed. The paper also delves into regulatory and ethical considerations, advocating for standardized frameworks and cross-industry collaboration to ensure AI safety. By integrating theoretical insights with practical recommendations, this study provides a roadmap for researchers and practitioners to fortify ML and RL systems against evolving threats. The ultimate goal is to foster trust and resilience in AI technologies, ensuring their safe deployment in diverse domains.