Road accidents remain a major public safety challenge, particularly for two-wheeler riders, where delayed emergency response and lack of real-time safety monitoring significantly increase injury severity and fatality risk. This paper proposes an AI-enabled smart helmet–based safety and monitoring framework designed to improve rider protection through continuous assessment of critical riding conditions. The proposed system focuses on three primary safety objectives: detection of accident-like events, identification of unsafe riding behaviour such as potential intoxication, and verification of helmet compliance. To enhance reliability and reduce false alerts, the framework incorporates sensor-fusion-driven machine learning that classifies riding events more accurately than conventional threshold-based approaches. In addition, the design supports a hybrid communication strategy to ensure emergency alerts can be triggered even under limited network availability, while also enabling optional cloud/dashboard-based visualization and long-term analytics. The proposed approach further introduces rider risk scoring and anomaly detection to provide preventive warnings and decision support. Overall, this work presents a scalable and research-oriented blueprint for intelligent rider safety systems that combines edge intelligence with real-time monitoring for improved road safety outcomes.