Mountain biking is an extreme sport with unpredictable terrain and several dangerous risks associated with it. Even the soundest minds might need external stimuli to alert them to be more careful at a particular moment of a potential fall. The proposed work involves developing an algorithm capable of detecting falls in mountain biking activity. Machine Learning classifier algorithms are used for fall detection. The existing fall detection algorithms are used to detect falls in environments with limited movements. Fall detection through the use of cameras causes invasion of privacy and is done in fixed environments with predictable dangers, another type is through sensors attached to the human body which acts as an obstruction to the activities of the person. The proposed Ensembled Boosting Model (EBM) classifier involves overcoming these pitfalls and developing a high accurate system to detect falls in open and unpredictable environments. The algorithm proposed in this paper aims to detect falls through real-time data such as acceleration, gyroscopic values, for any user. In the future, this algorithm can be used as a precursor to implement a real-time fall prediction device to be used by anyone and in any environment.