Applications of Machine Learning Across Smart Manufacturing, Healthcare, Finance, Computer Vision, Robotics, and Environmental & Sustainability: A Systematic Literature Review

Narjes Sadeghiamirshahidi, Seyedeh Elham Kamali, Bhavani Rath Reddy Dere

Machine learning (ML) has become a central enabler of data-driven decision-making across smart manufacturing, healthcare, finance, computer vision, robotics, and environmental sustainability. Despite the rapid growth of ML applications, existing review studies remain largely domain-specific and provide limited cross-domain synthesis of methodological trends, deployment challenges, and emerging research directions. This systematic literature review aims to provide a comprehensive and comparative analysis of ML applications across seven high-impact domains while identifying dominant learning paradigms, implementation challenges, and future research opportunities. Following the PRISMA 2020 guidelines, peer-reviewed studies published between 2015 and 2025 were systematically collected from major scientific databases, including ScienceDirect, IEEE Xplore, SpringerLink, Wiley Online Library, MDPI, and Web of Science. Studies were screened using predefined inclusion and exclusion criteria and categorized according to application domain, ML paradigm, algorithm type, data characteristics, and deployment context. The findings indicate that supervised learning and deep learning dominate most application areas, with convolutional neural networks emerging as the primary approach for image-based and perception-driven tasks. Reinforcement learning, although highly promising for sequential decision-making and adaptive control, remains comparatively underutilized due to safety, computational, and deployment constraints. Across domains, recurring challenges include data quality, interpretability, scalability, model robustness, computational requirements, and ethical considerations. Overall, this review provides a structured cross-domain synthesis of ML applications and highlights the growing importance of explainable, trustworthy, and deployable AI systems for future intelligent and sustainable technologies.