The rapid growth of E-Commerce has led to an unprecedented increase in cyber threats and data breaches, compromising sensitive customer information, undermining trust in online transactions, and resulting in significant financial losses. To address this critical challenge, this study explores the strategic innovation of Artificial Intelligence (AI) and Machine Learning (ML) for enhancing ecommerce data security. By leveraging AI-powered predictive analytics, ML-driven anomaly detection, and natural language processing, ecommerce businesses can proactively identify and mitigate potential security threats, ensure the integrity and confidentiality of customer data, and maintain a competitive advantage in the digital marketplace. This research provides a comprehensive framework for implementing AI and ML-driven data security strategies in ecommerce, encompassing AI-powered threat detection, predictive analytics, anomaly detection, data encryption and access control, and incident response and recovery. By adopting this framework, ecommerce businesses can significantly enhance their data security posture, protect sensitive customer information, and maintain a competitive advantage in the digital marketplace. This study contributes to the existing body of knowledge on AI and ML in ecommerce data security, providing actionable insights and recommendations for practitioners, policymakers, and researchers.