In the dynamic setting of T20 International (T20i) cricket, recognizing fielding players during the final overs poses major challenges due to poor lighting, occlusions, motion blur, and non-frontal views. CricXpert addresses this by introducing a hybrid AI system that combines computer vision with natural language processing to ensure accurate player recognition and intuitive access to player statistics. The system follows a multi-stage recognition pipeline: it begins with Optical Character Recognition (OCR) using EAST and Tesseract to detect jersey text. If OCR fails, it falls back to facial recognition, employing MTCNN for detection, FaceNet for embedding, and SVM for classification—achieving 95.83% accuracy. To improve generalization, a spatial model extracts ResNet50 features and classifies them via a stacking ensemble of SVM, KNN, and logistic regression, resulting in 98.14% accuracy. For movement-intensive clips, a GRU-based temporal gait model identifies players using engineered pose features such as joint angles and step length, reaching 95% accuracy. Beyond vision, CricXpert integrates a natural language interface for stat retrieval. Using GPT-4o with LangChain and Pydantic, it converts user questions into SQL queries. OutputFixingParser and schema-aware prompts minimize hallucinations, achieving an 85–90% query success rate on 100 test questions—outperforming LLaMA 13B and Gemini. The system has been validated using performance metrics and expert feedback from national-level coaches. CricXpert’s modular design allows scalability to other match formats and sports, marking a significant advancement in real-time sports analytics and AI-assisted decision support.