In the evolving customer support domain, traditional ticketing systems struggle to meet increasing demands for speed and accuracy. This study presents an intelligent ticket assignment system leveraging BERT, Graph Neural Networks (GNN), and Prototypical Networks to enhance classification and routing efficiency. The methodology includes comprehensive preprocessing of historical ticket data, feature extraction using natural language processing (NLP), and model evaluation based on accuracy, precision, recall, and F1-score. Results indicate that BERT achieves the highest accuracy (89.4%), precision (88.7%), recall (90.2%), and F1-score (89.4%), outperforming GNN (87.6%) and Prototypical Networks (86.8%) by notable margins. A comparative analysis with Random Forest (85.3%) further demonstrates a 4.1% improvement in accuracy. The analysis demonstrates both performance strengths and real-life practicality and scalability characteristics of the system when managing high traffic volumes. Stability and predictive accuracy improved through the application of noise filtering alongside SMOTE oversampling and weighted loss functions for addressing data quality problems and class imbalance and model integration complexities. The research demonstrates how machine learning changes the way customer service operations work while showing AI models can boost service quality and operational effectiveness