Abstract Manual customer complaint handling is often characterized by inefficiency, human error, and inconsistent categorization. This project presents Triago, an intelligent platform designed to automate the intake, analysis, and classification of complaints using Natural Language Processing (NLP) and Machine Learning (ML). The system leverages a robust technological stack, including Python for backend development (Flask/Django) and PostgreSQL for data management, alongside advanced ML frameworks (TensorFlow, PyTorch, Scikit-learn, SpaCy, and NLTK). Key functionalities include automated text preprocessing, intelligent complaint classification, sentiment-based priority assignment, and an analytical dashboard for performance monitoring. By shifting from manual processes to an automated framework, Triago enhances service quality, improves response times, and enables data-driven root-cause analysis of service deficiencies. This version is archived in the Arab International University (AIU) repository for open access and dissemination purposes. The content of this paper has not been modified from the original publication.For more information, please visit the official repository of Arab International University (AIU): https://www.aiu.edu.sy