Variational inference is a technique in Bayesian statistics that approximates complex posterior distributions through optimization, offering a scalable alternative to traditional sampling methods like Markov Chain Monte Carlo. It transforms the inference problem into an optimization problem by introducing a family of simpler distributions and finding the closest match to the true posterior using the Kullback-Leibler divergence.