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In the aircraft engine Maintenance, Repair, and Overhaul (MRO) process, effective task planning relies heavily on the expertise of lead engineers. However, when predictive models are used to assist decision-making, issues with incomplete, unbalanced, and inconsistent data can lead to errors in the planning task. Therefore, reliable predictions are crucial for optimising the operational efficiency. This paper proposes a methodology to improve the prediction of maintenance times for an aircraft engine MRO process by integrating K-means clustering with Cosine and Jaccard similarity methods to define a reliable prediction interval. This methodology was compared with the predictions of the Simple Linear Regression method, which resulted in the prediction interval approach significantly reducing prediction errors, increasing prediction accuracy, while optimising process management and maintenance task planning throughout the MRO process.