Predicting forest fire occurrence is essential for proactive disaster preparedness and environmental protection. We introduce a machine learning-based system that forecasts next-day fire probability at high spatial resolution using satellite-derived, multi-modal geospatial data. In contrast to existing reactive systems that rely on thermal anomaly detection (e.g., MODIS or VIIRS-SNPP), our approach is fully predictive, generating pixel-wise fire risk maps a day in advance. Our study focuses on Uttarakhand, India, which is an ecologically sensitive region that experiences frequent and severe forest fires. We curated a domain-specific geospatial dataset spanning 1 April to 29 May 2016. It includes daily 30-meter GeoTIFF images with 10 bands comprising weather (e.g., temperature, wind, precipitation), topography (slope, aspect), fuel map, and fire mask. We constructed this dataset from diverse sources and aligned all bands spatially and temporally. To demonstrate the usefulness of this dataset, we implement a deep convolutional neural network (CNN) using the ResUNet-a architecture, chosen for its robust performance in the semantic segmentation of high-resolution remote sensing data. Our model is trained from scratch to produce high-resolution fire probability maps and classify fire/no-fire pixels. Our solution helps with planning and decision-making for early intervention, especially in areas with high risk. It supports UN’s SDG 13 (Climate Action) and SDG 15 (Life on Land) by enhancing resilience and conserving ecosystems. The presented dataset and methodology can serve as a benchmark for future research on wildfire risk prediction using Earth observation data.