In the context of industrial maintenance, accurate criticality assessment of equipment plays a crucial role in deploying predictive maintenance programs, optimizing resource allocation and enhancing overall operational efficiency by taking well-aligned decisions regarding the equipment. Traditional approaches often rely on single-criterion evaluations, which can lead to biased or incomplete assessments. This paper proposes a robust multicriteria approach that combines the Analytic Hierarchy Process (AHP) with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to provide a more comprehensive and nuanced criticality assessment. By integrating multiple dimensions of criticality - specifically reliability, production impact, and maintenance costs - this hybrid method aims to overcome the limitations of conventional techniques. The study utilizes real industrial data, demonstrating the practical applicability of the approach. Furthermore, the paper includes a comparative analysis using Pareto and statistical quartile analyses to validate the effectiveness of the proposed method against single-criterion assessments. This research contributes to the field of maintenance decision support by offering a more robust tool for prioritizing equipment and quantifying criticality, potentially leading to more informed maintenance strategies and improved industrial resource utilization.