<p>This research scrutinizes a microfabricated thermoresistive calorimetric flow sensor by applying computational fluid dynamics (CFD) modeling, multi-objective optimization, and genetic programming-based regression. The CFD model is instrumental in conducting a parametric analysis of the sensor’s geometry and performance under varying inlet conditions. Through multi-objective optimization strategies, the objective is to maximize sensitivity while minimizing power consumption, leading to the identification of optimal design parameters. The resultant optimized dataset trains a genetic programming algorithm, facilitating the derivation of analytical relations between inlet conditions and optimal geometry. Insights into the sensor’s behavior are gleaned from simulations, and the amalgamation of optimization and machine learning expedites the design process. Particularly noteworthy is the heightened precision demonstrated by the sensor at low velocities ranging from 1 to 6&#xa0;m s⁻¹, rendering it well-suited for biomedical applications. This interdisciplinary methodology marks a significant stride in advancing sensor technology, leveraging the combination of numerical simulation, optimization, and data-driven modeling.</p>