The Internet of Medical Things (IoMT) has transformed healthcare delivery but faces critical challenges, including cybersecurity threats that endanger patient safety and data integrity. Intrusion Detection Systems (IDS) are essential for protecting IoMT networks, yet conventional models often struggle with class imbalance, lack interpretability, and are unsuitable for real-world deployment in sensitive healthcare settings. This study aims to develop an innovative, explainable IDS framework tailored for IoMT systems that ensures both high detection accuracy and model transparency. The proposed approach integrates a hybrid random sampling technique to mitigate class imbalance, Recursive Feature Elimination (RFE) for feature selection, and an optimized XGBoost classifier for robust attack detection. Explainable AI techniques, namely SHAP and LIME, are employed to provide global and local insights into model predictions, enhancing interpretability and trustworthiness. The system was evaluated using the WUSTL-EHMS-2020 dataset, which contains network flow and biometric data, achieving outstanding performance: 99.22% accuracy, 98.35% precision, 99.91% recall, 99.12% F1-score, and 100% ROC-AUC. The proposed framework outperforms several traditional Machine Learning (ML) models and state-of-the-art IDS approaches, demonstrating its robustness and suitability for practical healthcare environments. By integrating advanced ML with explainable AI, this work addresses the critical need for secure, interpretable, and high-performing IDS solutions in IoMT systems. The study concludes that explainability is not an optional feature but a fundamental requirement in healthcare cybersecurity, and the proposed framework represents a significant step towards safer and more accountable AI-driven security solutions for the IoMT ecosystem.