Onions have a high moisture content, which makes them more susceptible to microbial growth. Drying is one of the postharvest preservation methods applied to decrease onion moisture content, thereby increasing its storage life. In this study, onions were peeled, washed, cut into quarters, hot water blanched, and pureed. The puree was further dried using two different drying methods: refractance window drying (RWD) (water temperature: 70 °C) and convective drying (CD) (50 °C). The puree was spread on prefabricated trays at varying thicknesses of 2 mm, 4 mm, and 6 mm. It was observed that, irrespective of the drying method, moisture ratio (MR) decreased and drying time and effective moisture diffusivity increased with respect to the thickness of the puree. In addition, the Lewis model and the Wang and Singh model showed the highest R² and lowest SEE value for RWD and CD, respectively. Moreover, the MR of onion puree during RWD and CD was predicted using a multi-layer feed-forward (MLF) artificial neural network (ANN) with a back-propagation algorithm. The result showed that the ANN model with 12 and 18 neurons in the hidden layer could predict the MR, with a high R² value for RWD and CD, respectively. The results also showed that the thickness of the puree and drying method significantly affected the physicochemical quality (color characteristics, pyruvic acid content, total phenolic content, total flavonoid content, antioxidant capacity, and hygroscopicity) of onion powder. It was concluded that RWD proved to be a better drying method than CD in terms of the quality of dried powder and reduced drying time. Irrespective of the drying method, 2 mm-thick puree dried yielded the best-dried onion powder in terms of physicochemical quality, as well yielding the lowest drying time. These samples were further analyzed for calculating the glass transition temperature.