Fire is a natural and influential force, playing a role as crucial as the sun and precipitation for forest regeneration in boreal forests. However, fire can also pose considerable risks to public safety and valuable assets. Climate change due to warmer and drier conditions has been raising frequency of large wildland fires in Canada and made comprehending wildland fire crucial. Consequently, this research aims to propose a framework that enhances wildland fire danger map construction using machine learning (ML). Our study employs Random Forest Regression with Recursive Feature Elimination and Cross-Validation (RFECV) to optimize feature selection and improve the accuracy of wildland fire danger mapping in the boreal Managed Forest (MF) region in Ontario incorporating 45 key static and dynamic variables. The static variables include proximity-based factors such as distance from roads, settlements, railways, rivers, and water bodies. Additionally, topographic features such as elevation, slope, aspect, and various indices derived from topography were incorporated to further improve our model. Dynamic variables considered include the Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Land Surface Temperature (LST) derived from MODIS. Furthermore, meteorological data obtained from ERA5 were incorporated to capture variations in environmental conditions. Evaluating the results of our model showed that the Root Mean Squared Error (RMSE), and R² were 0.12, and 0.93, respectively. We applied a post-classification 3×3 pixel focal analysis to integrate localized regional effects into the final danger mapping, classified wildland fire danger maps with ordinal risk categories: very low, low, moderate, high, and very high risk. The results suggest that applying a post-classification approach with natural breaks enhances fire danger mapping, producing representations that more reflect real conditions in fire-prone areas compared to using natural breaks alone.