Additively manufactured wearable biosensors enable continuous, noninvasive, and real-time monitoring of physiological and biochemical signals during daily use. Recent advances in artificial intelligence (AI) provide complementary tools for identifying printable material windows, optimizing formulations and printing parameters, monitoring fabrication quality, and interpreting noisy, high-dimensional sensing data. These capabilities are particularly relevant to wearable biosensors, whose performance depends on manufacturing reproducibility and reliable signal analysis during continuous or multimodal operation. This review summarizes recent progress in the AI-assisted development of additively manufactured wearable biosensors across three stages: pre-printing design, in-process monitoring and control, and post-printing signal interpretation and application. By organizing the field around this workflow, we clarify how AI supports material selection, fabrication optimization, device evaluation, and wearable sensing, and identify the remaining challenges in developing more reliable and adaptive biosensing systems.