Forest fires severely affect many ecosystems every year, leading to large environmental damages, casualties, and economic losses. Emerging and established technologies are used to help wildfire analysts determine fire behavior and spread, aiming at more accurate prediction results and efficient use of resources in fire fighting. We propose a novel forest fire spread prediction platform based on a proven two-stage prediction model devised to deal with input data uncertainties. The model is able to calibrate the unknown parameters based on the real observed data using an iterative process. Since this calibration is compute-intensive and due to the unpredictability of urgent computing needs, we exploit an elastic and scalable cloud-based solution platform implemented through coarse-grain parallel processing using a work queue.