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.