Predictive AI for Household Hazard Prevention

Abstract

The increasing frequency and complexity of household hazards, including fire outbreaks, environmental exposure, and infrastructure-related accidents, have intensified the need for proactive and intelligent prevention mechanisms. Predictive artificial intelligence (AI) offers a transformative approach by enabling early identification of risk patterns through the integration of heterogeneous data sources such as sensor streams, environmental indicators, and behavioral signals. This paper examines the application of predictive AI models for household hazard prevention, drawing on established risk prediction frameworks from disaster management, healthcare, transportation safety, and smart infrastructure systems. By synthesizing insights from machine learning–based early warning systems and validated predictive models, the study highlights how AI-driven risk scoring and forecasting can enhance household preparedness and reduce vulnerability. The paper further discusses governance, ethical, and policy considerations, including data privacy, model transparency, and equitable access to predictive safety technologies. The findings underscore the potential of predictive AI to shift household safety strategies from reactive response to anticipatory risk management, contributing to broader resilience and sustainable risk reduction objectives.