Abstract: Hazardous environments are characterized by uncertain, rapidly changing conditions, where autonomous robots are responsible for making safety-critical navigation decisions to ensure a safer path. When navigation relies solely on reactive perception, a robot may encounter dangerous situations before it has sufficient time to respond effectively. In real-world settings, however, hazards such as smoke, heat, flooding, or structural instability can be observed directly by the environment itself through cameras and sensors. This creates an opportunity to anticipate risk in advance, before a robot enters unsafe regions. In this work, we focus on enabling proactive and risk-aware robot navigation through environment-centric perception. We present a unified multimodal spatiotemporal model that estimates navigation risk by fusing environmental visual and sensory data through early integration, producing instantaneous probabilistic risk estimates. To avoid reacting to short-lived disturbances and sensor noise, these estimates are incorporated into a Bayesian filtering process that treats risk as an underlying latent state and maintains a stable belief over evolving environmental hazards. The resulting risk belief is integrated into the robot’s navigation stack as a cost term, guiding the planner toward safer routes and allowing the robot to avoid hazardous areas before reaching them. We evaluate the proposed approach on a physical multi-hazard testbed that includes smoke, heat, water presence, vibration, and structural obstacles. Experimental results show reduced hazard exposure and safer navigation behavior compared to risk-unaware baselines, highlighting the benefits of environment-centric risk inference for autonomous robot navigation.