The Metropolis-Hastings Algorithm is a Markov Chain Monte Carlo method used to generate a sequence of samples from a probability distribution for which direct sampling is difficult, often employed in Bayesian inference and statistical physics. It iteratively proposes a candidate state based on a proposal distribution and decides whether to accept or reject it based on an acceptance ratio, ensuring the samples converge to the target distribution over time.