This paper reviews the applications and challenges of Artificial Intelligence (AI) and Machine Learning (ML) in Industry 4.0 (IR4.0), focusing on medical diagnosis, smart manufacturing, self-driving autonomous vehicles, smart cities, and smart homes. AI and ML are crucial for analyzing large datasets, automating processes, and driving innovation across various sectors. In medical diagnosis, these technologies enhance accuracy and efficiency by analyzing patient records and imaging data. In smart manufacturing, they improve quality control, supply chain management, and predictive maintenance. For self-driving autonomous vehicles, AI enables quick navigation and obstacle avoidance, enhancing safety and traffic flow. In smart cities, AI and ML optimize traffic management, public safety, and resource allocation. In smart homes, they enhance energy efficiency, security, and user convenience. Despite their benefits, challenges such as interpretability, data security, regulatory hurdles, and ethical considerations must be addressed to fully realize the potential of AI and ML in these fields. The paper also highlights the prospects and future opportunities for these technologies in IR4.0.This paper reviews the applications and challenges of Artificial Intelligence (AI) and Machine Learning (ML) in Industry 4.0 (IR4.0), focusing on medical diagnosis, smart manufacturing, self-driving autonomous vehicles, smart cities, and smart homes. AI and ML are crucial for analyzing large datasets, automating processes, and driving innovation across various sectors. In medical diagnosis, these technologies enhance accuracy and efficiency by analyzing patient records and imaging data. In smart manufacturing, they improve quality control, supply chain management, and predictive maintenance. For self-driving autonomous vehicles, AI enables quick navigation and obstacle avoidance, enhancing safety and traffic flow. In smart cities, AI and ML optimize traffic management, public safety, and resource allocation. In smart homes, they enhance energy efficiency, security, and user convenience. Despite their benefits, challenges such as interpretability, data security, regulatory hurdles, and ethical considerations must be addressed to fully realize the potential of AI and ML in these fields. The paper also highlights the prospects and future opportunities for these technologies in IR4.0.