The life insurance industry is at a tipping point and there is a paradigm shift as companies are adopting Machine Learning (ML), Artificial Intelligence (AI) to strengthen predictive analytics and risk management frameworks. Rapid Technologies are cheap-and-cheerful approaches for interrogating large volumes of data and enable insurers to predict customer behavior, predict risk, and make underwriting and claims processing more efficient." This paper explores the uses of ML and AI in life insurance including predictive modeling, fraud detection, customer segmentation, and personalized risk assessment. Hence, in this paper, we demonstrate through case studies and empirical researches how AI models can mitigate operational risks to support profit, decision making. We further explore AI adoption challenges tied to data quality, regulatory restrictions and the ethical concerns around deploying algorithms to make decisions. There is evidence that AI and ML can revolutionize life insurance risk management, giving insurers the ability to predict new risks or offer more customization in their products to consumers. Finally, the study closes with a discussion on future research directions on how AI could be combined with upcoming technologies like blockchain in dealing with data security and life insurance business processes transparency.