The research paper presents a framework which uses machine learning to study user behavior and predict mobile application faults and resource use. Researchers aim to achieve two goals through their work which includes predicting application failures and determining system resource needs using user interaction data and system operational data and contextual information. The framework uses three feature extraction methods which include statistical and temporal and sequence-based approaches to create models that show how applications function through both session-based and time-based data. The study evaluates four classification models which include Random Forest, XGBoost, Multi-Layer Perceptron (MLP), and LSTM for their ability to predict faults. For resource consumption forecasting, the study applies regression and sequence models which include Random Forest Regressor, XGBoost, and LSTM. Experimental results on a real-world mobile telemetry dataset show that XGBoost achieves the best fault prediction performance with an accuracy of 0.94 and a precision of 0.82 and a recall of 0.75 and an F1-score of 0.78 and an ROC-AUC of 0.93. LSTM model achieves better resource prediction results than other models by achieving 2.3% RMSE and 0.90 R² for battery prediction and 8.0% RMSE with 0.88 R² for CPU utilization. The research results demonstrate that using behavioral features together with telemetry data and contextual information leads to better prediction accuracy. The proposed framework offers a practical method which helps to improve mobile application reliability and efficient resource management and real-time system deployment.