<p>The rising population demands increased agricultural output, making groundnut metering devices essential for efficient sowing. Device performance hinges on key factors including the inclination angle, seed fill, moisture content, and operating speed, which collectively optimize seed distribution and enhance crop yield. Thus, understanding the significance of each parameter, modelling and optimization of parameters helps to enhance the device’s performance. Therefore, current investigation aims to present the effect of parameters on responses viz. cell fill and missing index using Taguchi’s design of experiments approach. Also, the prediction models using regression and artificial neural network (ANN) techniques were developed and presented. The analysis of experimental results using ANOVA technique shown that seed metering plate inclination has the highest significant effect on cell fill and missing index, followed by speed of operation and seed level. Main effect plots depicted that, inclination angle has significant impact on the responses. The increase in angle increases the cell fill and decreases missing index drastically. Regression and ANN model were developed and their prediction results were compared. The R-values and R-Sq. values of the models indicated, they were adequate to predict the responses. Besides, ANN model has shown superior prediction efficiency compared to regression model. Furthermore, TOPSIS, which is MCDM approach was used in the studied to determine the optimal parameter conditions for maximum cell fill and minimum missing index. It suggested that, 45° inclination angle, speed of operation about 1.5&#xa0;m/s, 75% level of seed in hopper and 8% moisture content are optimal parameter levels.</p>