Machine Learning (ML) is one of the most transformational technologies, significantly impacting various domains globally, including emergency services. This study leverages ML to forecast fire department interventions in the French regions of Ain, Doubs, and Yvelines, driven by the need to address economic challenges and budget cuts affecting fire services. By focusing on optimizing resource allocation through accurate intervention predictions, the study aims to enhance the efficiency of fire services. Given the increasing demand for fire services and the constraints of reduced resources, the study utilizes a comprehensive dataset that includes weather conditions, date/time specifics, and intervention details. Advanced ML models, such as XGBoost, LSTM networks, and LightGBM, were applied after a rigorous feature selection process. This process employed techniques like SelectKBest, RandomForestClassifier, and Lasso regression to identify the most relevant predictors. The results demonstrated high predictive accuracy, effectively capturing temporal and weather-related patterns to provide robust forecasts of fire department interventions. These findings underscore the potential of ML to modernize emergency services, allowing fire departments to anticipate and respond to emergencies more efficiently. Consequently, accurate intervention forecasts enable optimal resource allocation, ensuring that fire departments are well-prepared for peak times and high-demand periods, even amidst financial constraints.