Bayesian optimization is a sequential strategy for finding the global optimum of expensive-to-evaluate black-box functions. It employs a probabilistic surrogate model, typically a Gaussian Process, and an acquisition function to guide subsequent sampling decisions, balancing exploration of uncertain regions and exploitation of promising areas for sample-efficient optimization. This methodology is significant for optimizing parameters in computationally costly experiments, simulations, or machine learning models where function evaluations are limited.