Asian Journal of Current Research

Forest fires are escalating in frequency, intensity, and economic impact under a warming climate, stressing the need for high‑resolution, proactive fire risk mapping. This paper proposes an end‑to‑end framework that fuses multi‑sensor remote sensing, meteorological reanalysis, topography, vegetation dynamics, human activity proxies, and fire history with deep learning models for spatially explicit fire risk prediction at daily to weekly lead times. We review the state of the art, describe a modular data engineering pipeline, compare candidate model families (CNN–LSTM/Temporal Convolution, Vision Transformers, U‑Net, Graph Neural Networks), and outline rigorous spatiotemporal validation protocols to avoid leakage. We present a complete experimental blueprint—feature engineering, class imbalance handling, metrics (AUC‑PR, TSS, brier, reliability), ablations, and uncertainty quantification—so that researchers and agencies can reproduce, adapt, and deploy in heterogeneous biomes. A reference implementation and open data recipe are described to facilitate translation to operations.