This study presents a comprehensive numerical and experimental analysis on microfluidic cell lysis through computational fluid dynamics (CFD), data‐driven modelling, and multi‐objective optimization. The proposed intelligent framework integrates artificial intelligence and CFD for data generation and extraction, alongside machine learning analysis and experimental studies for transport phenomena characterization in the cell lysis process. The framework explores compound effects of various inflow Reynolds numbers and geometrical parameters, including obstacle configurations and microchannel thickness. It shows substantial effects on flow patterns and mixing in varied microfluidic designs. A surrogate model, developed via central composite design, exhibits high accuracy in assessing system functionality (). The height of the implemented baffles from its lower value to the upper bound resulted in more than 42% and 14% increase in the mixing index at low and high Reynolds numbers, respectively, with minimal impact on pressure drop. The framework introduces data‐driven modelling coupled with multi‐objective optimization by desirability function (DF), non‐dominated sorting genetic algorithm (NSGA‐II), and differential evolution (DE). In the optimization of microfluidic processes, machine learning algorithms outperform desirability‐based methods, and the DE algorithm surpasses the NSGA‐II. An optimum micromixing reducing the mixing length by over 50% and mixing index above 97% achieved, fabricated, and experimental investigations conducted to validate numerical process. Through the precise control of microfluidic variables and the exploitation of microtransfer phenomena, it is possible to enhance the efficiency and selectivity of cell lysis. This not only improves the accuracy of diagnostic information but also opens up new avenues for personalized medicine and therapeutic development.
