  Recent advancements in technology have led to the emergence of Cyber-Physical
Systems (CPS), which seamlessly integrate the cyber and physical domains in
various sectors such as agriculture, autonomous systems, and healthcare. This
integration presents opportunities for enhanced efficiency and automation
through the utilization of artificial intelligence (AI) and machine learning
(ML). However, the complexity of CPS brings forth challenges related to
transparency, bias, and trust in AI-enabled decision-making processes. This
research explores the significance of AI and ML in enabling CPS in these
domains and addresses the challenges associated with interpreting and trusting
AI systems within CPS. Specifically, the role of explainable AI (XAI) in
enhancing trustworthiness and reliability in AI-enabled decision-making
processes is discussed. Key challenges such as transparency, security, and
privacy are identified, along with the necessity of building trust through
transparency, accountability, and ethical considerations.
