Leak detection in industrial environments is critical for ensuring safety, environmental protection, and operational efficiency. This paper presents a comprehensive architectural framework for AI-powered leak detection and alerting within continuous monitoring systems. By integrating diverse sensor technologies with advanced machine learning algorithms, the proposed design enhances the accuracy and responsiveness of leak identification while addressing challenges such as data heterogeneity, real-time processing, and system scalability. Key components, including edge computing units, centralized analytics, and intelligent alert mechanisms, work synergistically to enable continuous, autonomous monitoring with timely, actionable alerts. The architecture’s modular and scalable nature supports adaptability across various industrial contexts, promoting resilience and operational reliability. This work further highlights the implications of adopting AI-driven monitoring systems for industry safety and environmental compliance, emphasizing improved risk management and proactive leak mitigation. Finally, the paper outlines future directions, including advancements in explainable AI, sensor innovation, and system interoperability, which are essential to enhancing the effectiveness and adoption of continuous leak detection technologies. Overall, this framework demonstrates how intelligent monitoring architectures can transform traditional leak detection approaches, fostering safer and more sustainable industrial operations.