Integrating Artificial Intelligence (AI) and Machine Learning (ML) with chaos engineering is essential for enhancing the resilience of software systems by proactively predicting and addressing failures. Chaos engineering involves intentionally introducing failures to uncover vulnerabilities, while AI/ML models analyze patterns from these disruptions to forecast and prevent future issues. This approach is particularly beneficial for modern AI/ML systems, which are inherently susceptible to various failures due to their interconnected nature. By collecting data from chaos experiments and using it to train predictive models, organizations can detect anomalies, predict potential failures, and take preemptive actions such as scaling resources or rerouting traffic. Real-world use cases include improving data pipeline resilience, managing resource constraints for inference workloads, and handling dependency failures. Best practices for integrating chaos engineering with AI/ML involve starting with isolated components, automating experiments, and continuously monitoring and iterating on strategies to enhance system reliability and fault tolerance.