The use of low-fidelity models usually leads to a reduction in the accuracy of the analyses, with the sole purpose of reducing computational time. On the other hand, using surrogate models for multi-objective problems requires specific algorithms and suitable sampling techniques capable of solving real-world engineering design problems. Research in developing surrogate models has experienced a remarkable increase in the last decade, especially for exploring the design space and supporting optimal designs. Recent research has applied heuristic optimization algorithms to solve the optimization problems associated with surrogate models [27; 28]. In addition, another way of building surrogate models using metaheuristic algorithms has been proposed [29; 30]. In line with previous limitations of surrogate models, new algorithms focused on Surrogate-Based Optimization (SBO) have been proposed [21]. Other novel research approaches are oriented towards evolutionary optimization, including multi-objective approaches. In this way, the surrogate employs not only the outcome from the expensive high-fidelity simulations but also involves the prediction of a much cheaper low-fidelity model, which could even be an analytical approximation [31]. The existing literature reviews [21; 32; 33] show that multi-fidelity surrogates have been applied essentially to optimization [10; 34; 35] and uncertainty quantification [36; 37], with a few works in which there is some degree of application to design space exploration [38; 39]. Unfortunately, useful surrogate methods for optimization may not be the right choices for design space approximation [25]. Furthermore, the performance of such surrogates is often assessed using synthetic and benchmark problems, so it is yet to be determined how to adequately employ multi-fidelity surrogates for original, authentic scientific, and engineering problems. The main contribution of this paper is the proposal of a systematic method using multi-fidelity surrogate models for space design approximation. The proposed methodology is applied in a problem of condensation mass flow in internal combustion engines, as an example of application in a complex engineering environment. Therefore, an original procedure is developed to assess different multi-fidelity surrogate models systematically using well-established error metrics. The developed method is presented using a step-by-step procedure employing thorough mathematical notation, so that the interested researchers can obtain a multi-fidelity surrogate model useful for their problem. Different designs of experiments are employed to obtain multi-purpose numerical campaigns that only consider the relevant parameters according to the objective of each campaign. The computational effort of the multi-fidelity strategy proposed in this work has been reduced. Indeed, the novel introduction of the metamodeled variable in the scope of space design exploration allows increasing the accuracy of the surrogate at the same cost. In this way, the method can cope with situations in which it is not affordable to explore many parameters of a space design using expensive simulations, such as those conducted using 3D CFD. The strategy is applied to the prediction of condensation when two humid air streams are mixed on a three-way junction with a twofold objective: to prove the method's validity in a real problem (in contrast with the synthetic examples employed in most of the works dealing with surrogates) and to set an example of how to apply the method. In this way, the results obtained from the numerical campaigns described in this work are available in the web version of this article. Determining the condensation mass flow rate generated by mixing two streams of moist air is relevant for air conditioning [40] and internal combustion engines [41]. Such condensation can be roughly estimated using low-fidelity models (0D models, as described in Section 4.2 or more accurately predicted using high-fidelity models (3D CFD simulations, as described in Section 4.3) following [41; 42].