The proliferation of real-time monitoring systems and the advent of Industrial Internet of Things (IIoT) over the past few years necessitates the development of scalable and parallel algorithms that help predict mechanical failures and remaining useful life of a manufacturing system or system components. Classical model-based prognostics require an in-depth physical understanding of the system of interest and oftentimes assume certain stochastic or random processes. To overcome the limitations of model-based methods, data-driven methods such as machine learning have been increasingly applied to prognostics and health management (PHM). While machine learning algorithms are able to build accurate predictive models, large volumes of training data are required. Consequently, machine learning techniques are not computationally efficient for data-driven PHM. The objective of this research is to create a novel approach for machinery prognostics using a cloud-based parallel machine learning algorithm. Specifically, one of the most popular machine learning algorithms (i.e., random forest) is applied to predict tool wear in dry milling operations. In addition, a parallel random forest algorithm is developed using the MapReduce framework and then implemented on the Amazon Elastic Compute Cloud. Experimental results have shown that the random forest algorithm can generate very accurate predictions. Moreover, significant speedup can be achieved by implementing the parallel random forest algorithm.