Abstract: The synthesis and characterization of silver nanoparticles have been progressively incorporated into undergraduate chemistry laboratory curricula in recent years, demonstrating both comprehensive and cutting-edge characteristics. Conventional teaching methods predominantly employ chemical reduction approaches, where silver ions are reduced to nanoparticles using mild reducing agents in glass vessels with water bath heating. However, this traditional methodology presents several limitations, including low reagent efficiency, inconsistent particle size distribution, and poor controllability over particle dimensions, all of which adversely affect experimental outcomes and student learning experiences. To enhance pedagogical effectiveness, this project introduces an integrated approach combining microfluidic chip-based synthesis with the guidance of machine learning. Precise control of flow rate, reactant concentration, and temperature parameters within microfluidic channels improves reaction reproducibility and controllability, significantly enhancing particle size uniformity and synthesis efficiency. Furthermore, machine learning algorithms are employed to model and predict experimental data, enabling students to comprehend how different reaction conditions influence product characteristics and achieve targeted nanoparticle synthesis. This innovative teaching design incorporates both microfluidic technology and data-driven artificial intelligence into traditional nanoparticle synthesis instruction, not only enriching course content but also elevating requirements for students’ operational skills, data processing capabilities, and problem-solving competencies. Through this implementation, the project aims to stimulate student interest in modern chemical research methodologies, facilitate deeper understanding of nanomaterial reaction mechanisms, and enhance scientific literacy and comprehensive innovation abilities.