The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and Computer Science. The book The text deals with tools and techniques used to predict extrapolate forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine Learning (ML). It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and Computer Science. Deep Learning (DL) is a subset of ML that involves a multi-layered artificial neural network (ANN). The working of ANN is inspired by how the nervous system of the human brain functions. A basic ANN consists of input, output, and hidden layers. Each layer consists of neurons, which resemble brain cells. A mathematical representation of the biological neuron is termed as “perceptron,” and thus, multi-layered neural network is also referred to as multi-layer perceptron (MLP) model.