Organizational safety in Industry 4.0 is a multifaceted domain that demands proactive monitoring to safeguard operations, employees, stakeholders and society at large. This article introduces a comprehensive conceptual framework for machine learning (ML), which enables proactive organizational safety monitoring and integrates research across multiple subdomains such as workplace safety, cybersecurity, sustainability, and financial risk management. ML-driven predictive analytics empowers organizations to track safety risks in real-time, leveraging international standards like ISO 31000 and ISO 45001 as theoretical foundations for feature engineering and ML model development. The proposed framework highlights the transformative potential of ML in enabling anomaly detection, threat classification and predictive action across diverse components of organizational safety. While the benefits of ML are evident, challenges such as industry-specific scalability, continuous model fine-tuning and mitigation of ML-specific threats require further research. Moreover, the human factor remains indispensable, as professional judgment and oversight will continue to play a critical role in ensuring the effectiveness of ML-enabled safety systems. This work aims to unify existing research and provide a novel, integrated perspective for advancing organizational safety in the modern era.