Falling is a severe hazard among older adults. Fall treatment is considered to be one of the most costly treatments, which usually extends to a long time. One bad fall can cause severe injuries that may lead to permanent disability or even death. Therefore, an efficient and cost-effective fall monitoring system is exceptionally indispensable. With the advancement in technology, wearable sensors and systems provide a lucrative way to continuously monitor the elderly people for detecting any fall incident that may occur. Most of these wearable fall monitoring systems focus only on detecting a fall incident. However, to avoid the risk of any future fall, it is essential to be aware of the cause of a fall incident also. Therefore, to address this challenge, a wearable sensor-based continuous fall monitoring system is proposed in this paper, which is capable of detecting a fall and identifying the falling pattern and the activity associated with the fall incident. The performance of the proposed scheme is investigated with a series of experiments using three machine learning algorithms, namely, <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-nearest neighbors (KNNs), support vector machine, and random forest (RF). The proposed methodology achieved the highest accuracy for fall detection, i.e., 99.80%, using KNNs classifier, whereas the highest accuracy achieved in recognizing different falling activities is 96.82% using RF classifier.