Risers are an important component in the oil s production and exploration field. They are responsible for the oil and gas transportation from the reservoir to the floating unit or injection of gas or water into the reservoir. The increasing the demand for this product has lead projects to explore to areas in which conditions are harsher. Typically, such a large project demands a large number of numerical finite element analyses and a great expertise from the engineer in charge in order to obtain a viable solution. This challenge leads engineers in search of consistent and reliable tools that assist in the early stages of the riser configuration design and are capable of reducing the number of total analyses required. One of these tools is application of optimization methods to obtain in a consistent and reliable manner the parameters which define a configuration. This work presents the Bayesian Optimization method, a method based on machine learning techniques capable of efficiently solving so called black box problems by exploring analytical approximations of the objective function, the function to be minimized. The method is applied to different case studies aiming to validate it as capable of solving a wide variety of riser configuration problems in an efficient and consistent way. Among the problems applied are different types of configurations, different realistic cases, mono-objective and multi-objective.