Wildfires cause economic, social, and environmental consequences, as they affect ecosystems, public safety, biodiversity, and natural resources. They pose challenges to various world regions, particularly Mediterranean areas such as Spain. Numerous fire prediction and detection systems were introduced to detect and predict fires as well as prevent their risks and damage. Statistical methods and classical machine learning models were often employed to estimate and predict fire risk, showing their efficiency in generating fire risk maps. However, they fail to accurately capture complex temporal and spatial characteristics related to fire ignition. To address this challenge, a novel Convolutional Neural Network (CNN) model, namely CFMap, was introduced for predicting and generating detailed wildfire risk maps covering Spain regions. Comprehensive analyses were performed using data between 2008 and 2024, including fire history, geographical location information, land usage features, human activity indices, topography data, meteorological features, and vegetation indices from Spain regions, collected from the IberFire dataset. CFMap showed a superior performance with an accuracy of 0.8028 ± 0.0440, an AUC (Area Under the Curve) of 0.9354 ± 0.0088, and an F1-score of 0.7787 ± 0.0623, outperforming classical machine learning methods (XGBoost, LightGBM, and RandomForest) and deep learning models including ResNet and a simple CNN. These results demonstrate its reliability in predicting fire events and generating monthly fire risk maps for different Spain regions. Consequently, it helps to identify high fire risk zones, improve fire management strategies, and efficiently deploy firefighting resources, thereby reducing the potential risk and impact of fires.