Gear pumps are fundamental components in hydraulic systems due to their robust construction, reliability, and cost-effectiveness. Despite their widespread industrial use, optimizing their performance under varying operating conditions remains a relevant research challenge. This paper presents an analytical evaluation of the 3PW-SEW-08-28-2-776 gear pump (Hydrotor), based on the results of experimental measurements conducted across a wide range of operational conditions. The study also introduces a conceptual framework for applying multi-valued logic tree methods to assess the relationships between key operating parameters and pump efficiency metrics. In a previous phase of research, conducted as part of a completed research project, genetic algorithms were used to optimize the gear tooth profile. Building on those findings, the current work proposes a data-driven decision modeling approach focused on identifying the influence of parameters such as fluid viscosity, discharge pressure, and rotational speed on volumetric, hydraulic-mechanical, and overall efficiency. The results indicate that elevated fluid temperatures correlate with reduced volumetric efficiency, while increased discharge pressures cause nonlinear torque variations, especially at low rotational speeds. The validation of the decision rules will be carried out using a reserved subset of the experimental data—specifically, parameter combinations (temperature, pressure, speed) that were not involved in the rule generation process. This ensures a robust assessment of the generalizability and reliability of the derived rules. Moreover, a risk analysis is presented, highlighting the sensitivity of the optimization process and the practical relevance of the decision logic. The presented work serves as both a continuation and an extension of previous studies, providing a foundation for the future use of machine learning in the diagnostic modeling and optimization of gear pump performance.