Earthquake damage prediction is essential to improve preparedness and resilience in seismically active regions such as Nepal. This study investigates the application of machine learning models to predict the grade of building damage during seismic events, using building attributes from the 2015 Gorkha earthquake dataset. We employed various algorithms, including Random Forest with both Random Oversampling and SMOTE, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boosting, and XGBoost, to tackle class imbalance in damage grades and enhance predictive accuracy. Key building attributes such as construction materials, number of floors, and building age were analyzed to forecast the severity of the damage, which was categorized into three grades. Our findings indicate that the Random Forest model, particularly with SMOTE oversampling, outperformed other techniques, achieving a macro F1-score of 0.67. Both Random Oversampling and SMOTE significantly improved performance over the imbalanced baseline, with Random Oversampling achieving a macro F1-score of 0.65. However, all models faced challenges in accurately predicting Grade 2 damage, underscoring the need for further refinement to address intermediate damage levels. The study highlights the strengths and limitations of various machine learning approaches and emphasizes the significance of feature selection and data balancing methods in improving earthquake damage prediction. It offers valuable insights for policymakers and engineers aiming to enhance earthquake preparedness and develop resilient infrastructure.