In this study, we aim to develop an integrated multi-quality characteristic optimization model that combines smart sensing technology with the Taguchi experimental design method to enhance the overall performance and quality stability of coffee grinding equipment. Five key quality indicators were selected: particle size uniformity, mean particle size, motor temperature rise, grinding time, and output mass. Real-time data feedback was enabled through the use of laser particle size analyzers, infrared temperature sensors, and electronic scales, ensuring the precise and continuous monitoring of operational parameters. The experiments employed an L18 (21 × 37) orthogonal array, covering five control factors: cooling fan operation, burr gap, motor speed, bean feed rate, and grinding time. After calculating the signal-to-noise ratios for each quality indicator, the analytic hierarchy process was applied to assign weights and perform weighted integration, allowing the identification of the optimal parameter combination. The results indicated that the optimal combination, namely, fan off, a burr gap of 1.0 mm, a motor speed of 1000 rpm, a feed rate of 10 g, and a grinding time of 30 s, achieved a validation error of only 0.22, demonstrating the model’s strong robustness and application potential. The outcomes of this research provide a solid foundation for the design of intelligent coffee equipment, as well as for the development of quality control systems and automated parameter adjustment mechanisms.