Abstract This review analyzes the evolution, current state, and future directions of fault detection and diagnosis (FDD) systems in the chemical industry, highlighting the challenges and opportunities associated with their development and implementation. A systematic review of FDD methodologies, including model‐based, data‐driven, hybrid, and AI‐driven approaches, was conducted to evaluate their strengths, limitations, and industrial applicability. While model‐based methods provide high interpretability, they struggle with scalability and complexity in large‐scale operations. Data‐driven techniques excel in handling nonlinear and complex processes but are limited by the need for large, high‐quality datasets. Hybrid and AI‐driven systems offer a combination of adaptability and scalability; however, they face computational and interpretability challenges. The study identifies significant barriers to the widespread adoption of intelligent FDD systems, including the complexity of chemical processes, real‐time processing demands, scalability issues, integration with legacy systems, economic constraints, and organizational resistance. Despite these challenges, emerging technologies such as IoT, big data analytics, and explainable AI (XAI) present promising opportunities to enhance fault detection accuracy, adaptability, and sustainability. The findings emphasize the importance of developing modular, scalable, and explainable FDD systems that can seamlessly integrate into existing industrial infrastructures. This review underscores the need for greater collaboration between academia and industry to align theoretical advancements with practical requirements, ensuring that FDD systems are both technically robust and industrially viable. By addressing these challenges and leveraging emerging technologies, FDD systems can play a pivotal role in driving safer, more efficient, and sustainable operations in the chemical industry.