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Micromixers are inevitable components in microfluidics, micro-electro-mechanical devices, and numerous bio-chemical assays. By assays, we mean diverse analytical procedures encompassing gene engineering, the manipulation of genetic material; nanoparticle synthesis, focusing on the controlled creation of nanoparticles; and cell lysis, involving cell membranes disruption for the release of intracellular substances for diagnostic purposes. In these assays, the homogeneous mixture of two or more fluids is crucial. However, designing an efficient micromixer providing high homogeneity and low pressure drop, while maintaining controllability, is challenging. Controllability refers to the design of a micro-system tailored to meet the specific requirements of a given assay. This study proposes a controllable framework, combining machine learning and statistical modeling. The framework begins with the generation of a reference parametric micro-structure, herein a microchannel with L-shaped baffles and featuring seven variables. A response surface method, a data-driven modeling scheme, is used to establish functional relationships between design variables and objective functions. The study reveals that the baffle height significantly impacts the system functionality, increasing the mixing index by over 40% and the pressure drop by more than 220% when reaching its upper limit. Dean-like secondary vortexes are generated in the microchannel at Re = 10, demonstrating the efficiency of the implemented baffles. Subsequently, multi-objective optimization methods, non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE), are employed, with adaptable variable constraints. Comparative analysis of the methods shows that DE finds superior optimum solutions in fewer iterations. Finally, an optimum structure is fabricated using soft lithography, and experimental tests are conducted for validation. [ABSTRACT FROM AUTHOR]