This chapter presents a data-driven Aircraft Health Monitoring System (AHMS) integrating Internet of Things (IoT) sensors and machine learning for real-time anomaly detection and predictive maintenance. The system achieved 96% accuracy in barometric pressure monitoring and reduced unscheduled maintenance by 30% in drone-based tests. Security protocols, including AES-256 encryption and TLS 1.3, mitigated aviation cybersecurity risks like GPS spoofing and data tampering. A cost–benefit analysis revealed a 20% reduction in operational costs ($12,000 deployment cost per aircraft) and an 18-month ROI. The study addresses gaps in existing AHMS frameworks by incorporating real-world validation, edge computing for latency reduction, and compliance with FAA/EASA cybersecurity standards.