Blasting powder factor is an important consideration when planning a drilling and blasting operation because it affects both the total production cost and the efficiency of downstream operations. Ten production blasts and 38 blast record datasets from a small scale dolomite quarry in Akoko Edo, Nigeria were studied to determine the effect of powder factor on blast fragmentation size distribution and uniformity index. This research evaluated the relationship between powder factor and fragmentation size distribution, as well as apply artificial neural network to model small diameter drill hole blast powder factor based on predominant controllable blast design factors. According to the research, the small scale blasting powder factor ranges between 0.6 and 1.0 kg/m3. According to the WipFrag analysis, increasing the powder factor causes an increase in the fragmentation mean size and an upward shift in the particle size distribution curve. The mine blasting operation uniformity index was also found to be between 1.3 and 1.68. The powder factor range of 0.7-0.9kg/m3 was determined by the uniformity index. The performance of the proposed ANN model was evaluated using the correlation coefficient and found to have a high prediction accuracy (R2 = 0.997). As a result, the proposed ANN models can be used to improve the blast powder factor for real-world applications.