Engineering systems are becoming increasingly complex, generating large amounts of data and requiring more sophisticated approaches to manage decisions, risks, and system performance throughout their lifecycle. Traditional Systems Engineering Management (SEM) methods provide a solid foundation, but they often struggle to fully leverage the potential of the data now available. In this context, data analysis and Artificial Intelligence (AI) offer new opportunities to support and improve engineering management activities. This thesis explores how data analysis and AI can be used to optimize Systems Engineering Management processes. The focus is on key areas such as requirements management, system design, risk management, decision-making, and performance monitoring across the system lifecycle.