This paper presents a detailed survey of adversarial attacks on machine learning models and corresponding defense mechanisms. It covers various attack vectors, including evasion and poisoning attacks, their impact on AI-driven systems, and state-of-the-art defensive strategies. Additionally, the paper discusses real-world applications and ethical considerations in adversarial AI research.