Industrial safety, particularly in high-risk sectors such as chemical industry, remains a critical concern due to the prevalence of catastrophic incidents caused by inadequate hazard detection and emergency response systems. This research investigates the limitations of current safety mechanisms in small to medium-scale industries and proposes a novel framework integrating autonomous drones, IoT sensors and AI-driven analytics. Through user-centred research involving interviews, surveys and contextual inquiries at industrial sites, the study identifies gaps in real-time monitoring and risk management. The proposed solution includes autonomous drones equipped with LiDAR and quantum sensors for real-time hazard detection, coupled with AI predictive analytics for proactive risk mitigation. A service-based website and desktop application provide seamless user interfaces for monitoring and emergency response coordination. The findings highlight significant improvements in safety, operational efficiency and scalability, addressing challenges faced by industries with limited access to advanced technologies. This study concludes with an analysis of implementation strategies, limitations such as cost and regulatory hurdles and recommendations for future research. By leveraging emerging technologies, the proposed framework aims to bridge the gap in industrial safety, ensuring a safer working environment while minimising risks to human life and the environment. The research offers a transformative approach to industrial safety management, providing practical, scalable and cost-effective solutions adaptable to various industrial settings.