The wildfires in fire-sensitive ecosystems and regions have ruined fertile land, causing consequences which could well be dealt with by vegetative and environmental regimes. Global climate change is altering fire regimes, most especially with regard to fire frequency and intensity and timing. Some ecosystems are shifting favorably toward fire-adapted species. Fire remains a dominant factor promoting deforestation and extinction on a global scale they frequently spread uncontrollable before suppression. Hence, a serious concern arises in the context of heavy destruction possible due to lack of wise prediction and immediate response to fire events. Forest fires interrupt vegetation and wildlife and usually seem to endanger human habitations and pose a considerable environmental threat. While certain ecosystems such as the grasslands and temperate forests have adapted to regular fires for their regeneration, uncontrolled wildfires devastate beyond recovery. The understanding of where fires will start or how intense they become contributes to minimizing their effect. The aim of this work is to create a machine learning (ML)-based model able to forecast the probability of fires from environmental variables and weather. The model takes into account elements including temperature, humidity, how fast and in what direction the wind blows, rain, Fire Weather Index (FWI), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Buildup Index (BUI), Fine Fuel Moisture Code (FFMC), and past fire events. The methods used to pick the most important factors (Extra Trees, Forward Selection, Chi-Square, Pearson Correlation) showed that FFMC, ISI, DC, BUI, and temperature play the biggest role in predicting fires. These factors kept showing up in all the methods, which shows they have a big effect on fire risk. The team chose these factors because they’re known to affect how fires behave and they’re available in the Algerian Forest Fires Dataset. This means the model looks at both weather and environmental things that can lead to wildfires. The aim of this study, therefore, is to build a ML-based model for predicting the risk of potential fires through meteorological and environmental parameters. By assessing the condition of the weather and its different factors, the model aims to predict the probability of fire outbreak and its intensity in particular regions. Accurate prediction of fires will enable prevention, monitoring, and more effective management strategies, which will help to conserve forests by minimizing fire-induced losses. The k-nearest neighbor model achieved the highest prediction of wildfire risk with an accuracy of 90% on the Algerian forest fire dataset. Its high predictability indicates a significant contribution to managing wildfire hazards while supporting proactive strategies for disaster management. Real-time decisions in case of wildfires can also be aided by the model alone for faster and proper operational responses.