Critical infrastructure networks (CINs), such as power and water networks, are the backbone of society and the economy. However, they are vulnerable to disruptions, such as component failures and natural disasters. These disruptions can spread across CINs, causing significant socioeconomic losses. Decision-makers face the challenge of protecting CINs before disruptions and restoring their functions afterward, considering interdependencies and uncertainties. Current methods struggle to model big data, complex interactions, and multilayer dependencies between CINs. Artificial intelligence (AI) and machine learning (ML) applications can be used to overcome these challenges. AI and ML can generate sophisticated failure prediction models using historical failure data, maintenance data, and current risk indicators, and can classify critical CINs components and their risk levels using multisource data-aggregation frameworks. The Saudi Vision 2030, the strategic roadmap launched by the Saudi government, aims to create a resilient and sustainable infrastructure. Using AI and ML to strengthen the resilience of CINs can help achieve this goal. This article explores trends and opportunities in this area that align with the Vision to help practitioners in the Kingdom position Saudi Arabia as a leader in the field.