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Abstract This study proposes an alternative approach to predict wave elevation near multi-column semi-submersible structures by applying machine-learning methods from experimental data. The most common approach to this problem is to apply linear potential theory numerical programs to calculate the wave elevation close to such marine structures. However, in some cases, the assumptions for the potential theory are no longer valid, leading to a deviation from model test results. Another possible approach is to solve Navier Stokes Equations Numerically (CFD), which poses a challenge regarding computational power for this kind of stochastic analysis. This paper details a procedure to apply machine learning to enhance the results by combining potential theory and experimental results for future predictions. Aker Solutions has performed experimental tests with a TLP (Tension Leg Platform) shaped hull under waves while measuring wave elevation on several points around it. These experimental data were treated and combined with potential theory results to compose a machine-learning prediction model. A frequency domain model was applied, where the experiment data is converted into the frequency domain and combined with the Potential Theory results to train the machine learning model. Results show that the machine learning model improves the results for wave elevations when closer to the hull, as those are the cases where the potential theory deviates more. This approach can also be applied to similar problems, such as wave loading and vessel motions.