<p>Microfluidics is a rapidly growing technology with applications across healthcare, environmental, energy, and other sectors, and laser-based processing of PMMA is widely used for fabricating such devices. PMMA possesses many unique features amenable to microfabrication and laser ablation of PMMA allows for creation of distinct fine features with high accuracy. Presently, these microfabrication processes are revolutionized by integrating machine learning techniques which enable design automation and seek optimization in manufacturing. Machine learning has a notable prospective for microfluidic device fabrication by consistently upgrading design and process parameters. The performance prediction of novel microchannel geometries by machine learning models based on pre-existing data and simulation results, facilitates an assessment of pressure drops, flow rates, and other aspects and eliminating recurrent physical testing. This work investigates the fabrication of microchannels in PMMA using a CO₂ laser by varying laser power, scan speed, PPI, and operation mode (raster/vector), and examines their effects on channel width, depth, and surface roughness. Laser power and scan speed were the dominant factors governing channel features. Furthermore, the prediction of these features was accomplished using a machine-learning approach. Linear regression, decision tree, random forest, k-NN, gradient boosting, and AdaBoost models were applied to the experimental dataset, and outlier detection was performed to enhance prediction accuracy. k-NN, random forest, and gradient boost delivered best R2 scores above 0.9 for width, depth, and surface roughness prediction analysis. From the findings, it can be emphasized that by leveraging predictive modeling, ML can significantly enhance the cost-effectiveness and quality of microfluidic device fabrication. In contrast to traditional methods, ML techniques may also assist in achieving innovative microchannel geometries thereby escalating device performance. </p>