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In recent years, electronic packaging has evolved significantly to meet demands for higher performance, lower costs, and smaller designs. This shift has led to heterogeneous packaging, which integrates chips of varying stack heights and results in non-uniform heat flux and temperature distributions. These conditions pose substantial thermal management challenges, as they can create large temperature gradients, which increase thermal stress and potentially compromise chip reliability. This study explores single-phase liquid cooling for multi-chip modules (MCMs) through a comprehensive experimental and machine learning approach. It investigates the impact of chip spacing, height, fluid flow rate, fluid inlet location, and heat flux uniformity on chip temperature and the thermohydraulic performance of a commercial cold plate. Results show that increasing coolant flow from 1 LPM to 2 LPM decreased thermal resistance by 26 %, with heat losses remaining below 5 %. The left inlet configuration improved temperature uniformity compared to the right, though both yielded comparable thermal performance. Adjusting heater spacing impacted temperature distribution based on inlet position, and lowering one heater by 1 mm raised its temperatures by 15 ◦C due to increased thermal resistance from thermal interface material. A transient test demonstrated the cold plate’s quick response to power surges, in which there is only a 1 ◦C spike above steady state. Complementing these findings, an Artificial Neural Network (ANN) model was developed with optimized architecture specifically for the unique challenges of this study. The ANN model was rigorously validated using an independent dataset, achieving highly accurate temperature predictions (R2 = 0.99) within 2.5 % of experimental