Snow removal dispatch decisions during winter nights require not only the accurate prediction of dispatch necessity but also the intuitive presentation of road conditions and decision-relevant information to human operators. In this study, we propose a snow removal dispatch decision support method that integrates fixed-camera images and meteorological information to both predict dispatch necessity and visualize information for operational support. The proposed method employs a machine learning model with feature selection, integrating meteorological observations, short-term forecasts, snow depth measurements, and an image-derived road condition indicator called the snow coverage ratio (SCR). From a sensing perspective, the proposed method integrates heterogeneous environmental sensing modalities—including fixed-camera imaging, snow-depth sensing, and meteorological sensing—to represent road surface conditions relevant to dispatch decision-making. By visualizing road conditions together with prediction results, the system provides operators with objective and consistent situational awareness to support decision-making. Experimental results confirm that SCR effectively reflects temporal changes in road conditions and contributes to dispatch prediction. Furthermore, evaluation using data collected under similar winter meteorological conditions shows that a dispatch prediction model with L1 regularization tends to outperform human operators’ decisions. Additional evaluation applying models trained on past-year data to subsequent years demonstrates that training with multi-year datasets improves generalization under varying winter conditions. These results indicate that the proposed method provides practical and reliable support for snow removal dispatch decisions in real operational environments.