This paper addresses the persistent rise in motorcycle-related fatalities, even as overall road deaths decline, by introducing an adaptive Fuzzy System based on the Takagi-Sugeno model. The system evaluates parameters such as acceleration and lean angle to classify rider behavior into categories such as normal, aggressive, or dangerous, providing timely feedback aimed at promoting safer driving practices. A key component of this approach is the Local Outlier Factor (LOF) algorithm, which identifies hazardous behaviors by quantifying deviations from standard riding patterns, thereby allowing the establishment of adaptive safety thresholds. By integrating fuzzy logic, the system offers refined decision-making capabilities in complex riding conditions, enhancing active safety systems such as traction and braking controls. This work emphasizes the critical role of behavior-based insights in mitigating accidents, particularly since rider actions are a major contributing factor to motorcycle incidents.