Poorly described support tickets are a major challenge in customer service systems, often leading to misclassification, inaccurate prioritization, and inconsistent resolution time estimation. Most existing systems handle ticket categorization and resolution time prediction as isolated functions, often relying on rule-based logic or static estimation methods, with limited ability to adapt to changing support patterns. This research proposes ClassiSolve, a unified adaptive system that simultaneously categorizes support tickets and predicts resolution times. The project was conducted using an Agile project management methodology, following an incremental and iterative approach that incorporates Feature-Driven Development (FDD) for modular design and Test-Driven Development (TDD) for reliability. The solution employs a supervised learning methodology, utilizing a single XGBoost model trained on preprocessed historical ticket data to perform both ticket categorization and resolution time prediction. To optimize workflow, ClassiSolve also integrates an automated agent assignment mechanism that recommends the most suitable handler for each ticket based on predicted category and staff skill alignment. Evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) confirmed the model’s accuracy and reliability. Real-world relevance was validated through expert interviews and surveys among IT professionals, revealing a strong industry need for adaptive systems. ClassiSolve’s dynamic learning capability enhances support team efficiency and accuracy by enabling continuous improvement. Observations indicate improved workload balance, faster resolution, and more consistent service quality, while conclusions confirm the effectiveness of a unified, data-driven model in addressing ticketing inefficiencies. The system offers a scalable and practical solution for modernizing IT support operations.