Joint probability distribution represents the probability of two or more random variables occurring simultaneously. In AI, understanding joint distributions is crucial for tasks involving multiple variables, such as in Bayesian networks. This concept helps model relationships between variables and is fundamental in statistical inference, enabling the prediction of outcomes based on interdependent factors.