AI-Based Safety Monitoring Systems for Risk Mitigation in Paint Manufacturing Industries: A Critical Review

Author(s)	Dr. Amir Ahmed Fadol, Hasabel rasolAbdelbag, Elsadig Elhadi, Mohamed Ahmed
Country	Sudan
Abstract	The paint manufacturing industry involves hazardous processes that expose laborers and facilities to apparent safety risks, including chemical exposure, fire and explosion hazards, mechanical accidents, fumes, and emissions. Traditional safety management approaches, which rely solely on manual inspections and rule-based monitoring, often fail to provide real-time risk prediction and proactive hazard mitigation. Current advances in artificial intelligence (AI), including machine learning, computer vision, IoT, and intelligent sensor networks, have enabled the development of AI-based safety monitoring systems capable of stringent surveillance, early hazard warning, and data-driven decision-making. This critical review examines the current state of AI-based safety monitoring technologies used in the paint manufacturing industries, with a focus on their role in risk identification, early warning, assessment, and mitigation.
Keywords	Artificial intelligence (AI); paint manufacturing; occupational safety; hazard identification; predictive analytics; safety monitoring systems.
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-19
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.71321

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