Agricultural big data and high-performance computing have significantly improved crop yield modeling. Maize growth dynamics and yield prediction are crucial for sustainable agriculture. This study introduces an advanced modeling approach utilizing Gradient Boosted Decision Trees (GBDT) combined with a feature selection strategy to predict maize biomass production. A dataset of 200 unique maize plants was observed throughout the vegetation season. Our approach integrates manual measurements, meteorological data, and vegetation indices along with Internet of Things (IoT) field sensors to perform spatio-temporal analysis. Results indicate that maize stalk thickness and height are the most reliable predictors of biomass yield, while environmental variables show minimal impact. The most effective model, period-dependent GBDT, demonstrated superior predictive performance, achieving an average error of 4.39 mm in plant growth predictions. Notably, stalk thickness and height can be estimated six weeks before harvest, while biomass yield two weeks before harvest. This research underscores the potential of machine learning and remote sensing to enhance precision agriculture decision-making.