The Forward Algorithm is a dynamic programming technique used in the field of Hidden Markov Models (HMMs). It calculates the probability of a sequence of observed events by summing over all possible hidden states. This algorithm efficiently computes the likelihood of the observed data given the model parameters, allowing for better predictions and understanding of the underlying processes. In the Forward Algorithm, each step involves updating probabilities based on previous states and the current observation. This iterative approach ensures that all possible paths through the model are considered, making it a powerful tool for tasks such as speech recognition and bioinformatics.