Running biomechanics was investigated using sensor technology to clarify the relationships between important parameters and injury prevention. Through secondary data analysis and case evaluations, validated thresholds of biomechanical variables were established: ground reaction force (GRF) loading rate (≤65 body weight/s), gait asymmetry index (≤15%), and ground contact time (≤250 ms). These thresholds can be used as indicators of running efficiency and injury risk. Sensor data demonstrated negligible measurement error and offered reliable biomechanical information, confirming their suitability for real‑time monitoring and intervention. GRF and loading rates were identified as essential predictors of injury susceptibility. Furthermore, machine learning models trained on sensor data accurately detected biomechanical abnormalities, supporting the integration of automated monitoring systems into injury prevention strategies. The sensor‑based approach enables evidence‑based guidelines for parameter interpretation, advances methodological validation, and promotes standardized mathematical modeling. It also facilitates intelligent monitoring programs, individualized training, and personalized prevention protocols. The effective integration of machine learning with wearable sensors requires devices capable of delivering real‑time, personalized feedback on cadence, ground contact time, and related metrics. To establish validated thresholds, three evaluations were conducted: the analysis of the Gutenberg Gait Database, the assessment of the Human Activity Recognition‑3 dataset, and the systematic meta‑analytical synthesis of 156 peer‑reviewed studies published between 2015 and 2025.