Landslides pose significant risks to infrastructure, ecosystems, and human lives, making accurate prediction crucial for disaster preparedness and mitigation. We integrate multimodal environmental data to enhance landslide prediction using machine learning. Specifically, we combine temporal weather data from ASOS, static vegetation data from NLCD, static soil composition data from SOLUS100, and temporal soil attributes from ERA5-Land to estimate landslide probability within a 5 km radius of ASOS weather stations across six U.S. states. We frame this as a multiclass classification problem, predicting high, low, or no landslide probability. Given the inherent imbalance in landslide occurrence, we explore various techniques such as SMOTE oversampling, class-weighted training, and dimensionality reduction to improve model performance. Our results indicate that XGBoost trained on SMOTE-balanced, PCA-reduced data incorporating all four datasets achieves the highest macro F1-score of 0.70. Analysis of feature importance reveals that significant predictors span all datasets, highlighting the necessity of integrating diverse environmental variables. Additionally, we conduct state-wise and seasonal comparisons to assess regional variations in model effectiveness. This research demonstrates the potential of multimodal data fusion and machine learning in landslide forecasting, paving the way for more robust and interpretable predictive models for natural hazard assessment.