Machine Learning‐Guided Near‐Infrared Laser Processing for Precise Geometry Control in Microneedles with Downstream microLED‐Triggered Photothermal Functionality

Transdermal drug delivery and sensing provide non-invasive routes for localized therapy and biomarker detection. Microneedles (MNs) enable pain-free access to subdermal layers; however, variations in skin thickness and stiffness across anatomical sites demand precise control over MN geometry. This study presents a machine learning-guided approach for predictive fabrication of MNs using near-infrared (NIR, 1064 nm) laser processing of duralumin. Laser power, repetition, and computer-aided design (CAD) dimensions are systematically mapped, and a data-driven model is trained to predict MN height and tip radius with high accuracy. Using this framework, application-specific MN geometries are selected prior to fabrication, reducing empirical trial-and-error. For example, a target MN height of 524 µm for the female chin is predicted with a 1.24% deviation from experimental measurements. The predictive metal masters are subsequently translated into polymer MNs, demonstrating reliable replication and geometric fidelity. To illustrate functional relevance, polyvinyl alcohol (PVA) MNs incorporating a near-infrared-responsive dye are fabricated and integrated with a flexible micro-light-emitting diode (LED) platform, enabling localized photothermal heating. This work establishes an end-to-end, data-driven pipeline linking laser parameters to functional MN devices, offering a scalable strategy for application-specific MN design across transdermal drug delivery, sensing, and bioelectronic interfaces.