This thesis, "From Data to Decisions: Enhancing Monthly Profit Predictions through XGBoost," addresses the critical need for accurate financial forecasting within multinational electronics manufacturers like Carlo Gavazzi Automation. Traditional forecasting struggles with complex business data, including seasonality, fluctuations, and missing values. The primary goal is to develop a robust, time-aware monthly gross profit forecasting model using XGBoost, aiming to significantly outperform simpler baselines. The methodology begins with systematic data preparation from Carlo Gavazzi Automation's granular sales records (2016-2024). Key cleaning steps included handling missing values and standardizing suspicious entries. Data types were converted: 'Fiscal Period' to datetime64[ns] and numerical columns to float64.