Recognising the critical role forests play in global biodiversity and the increasing threat of wildfires, this work exploits advanced geoscientific technologies and machine learning techniques to improve fire risk prediction and management. The primary objective is to develop a Convolutional Neural Network (CNN) that maps remotely sensed images to fire risk levels using a refined subset of the FireRisk dataset. The employed dataset contains 7,644 images categorised into five fire risk classes. Based on it, this work benchmarks the performance of InceptionResNetV2 and Vision Transformer models, which have been pre-trained on extensive datasets and fine-tuned for fire risk classification. The achieved custom CNN model achieves an accuracy and F1 score of 72%, demonstrating its potential for this application.