In Agile software development, maintaining velocity requires the continuous management of Technical Debt (TD). However, the rapid iteration cycles inherent to Agile often obscure debt accumulation, making manual identification in issue trackers prohibitively expensive. To address this, we present TD-Suite, a comprehensive framework engineered to automate the classification of technical debt. It leverages state-of-the-art transformer models to analyze textual artifacts, such as developer discussions in issue reports, where subtle indicators of debt often lie hidden. TD-Suite provides a seamless end-to-end pipeline suitable for Agile ML Engineering, managing everything from initial data ingestion and rigorous preprocessing to model training, thorough evaluation, and final inference. It supports both binary classification (debt or no debt) and granular categorization—identifying code, architecture, design, or documentation debt—enabling Agile teams to formulate targeted refactoring strategies. To ensure robustness on real-world, imbalanced datasets, TD-Suite incorporates k-fold cross-validation, early stopping, and class weighting strategies. The framework explicitly integrates the tracking and reporting of carbon emissions associated with model training. Furthermore, it features a user-friendly Gradio web interface within a Docker container, simplifying integration into DevOps pipelines and democratizing access for practitioners without deep ML expertise.