The growth/no growth boundary model of Bacillus simplex was developed using logistic regression and neural network as a function of pH, heating temperature, and water activity (aw). The model was based on the bacterial responses in tryptic soy broth (TSB) with an initial bacterial count of ca. 10 CFU/mL, which was in turn dependent on 192 conditions comprising four levels each of pH (7.0, 6.6, 6.2, and 5.8), heating temperature (70, 80, 85, and 90 °C for each 10 min), and storage period (1, 2, 3, and 4 weeks), and three levels of aw (0.97, 0.98, and 0.99). To evaluate definite growth probability, 60 repetitive experiments were performed per condition, which resulted in total 11,520 datasets. The developed models were evaluated using independent experimental dataset of growth/no growth in TSB and carbonara sauce, an example of nutrient-rich processed food matrix. Both developed models accurately described the growth/no growth boundary of B. simplex. Although all the four parameters significantly influenced the growth/no growth boundary, the heating temperature and storage period exhibited greater effect than pH and aw parameters under the examined conditions. The fraction correct (FC) values of independent verification data in TSB for the logistic regression model and the neural network model were 92.2% and 89.8%, respectively. Developing a model with three replicated experiments, which are conventionally and frequently used in the previous studies, may lead to incorrect judgement of growth/no growth. Models based on such inaccurate datasets result in inaccurate prediction. For example, the area under the receiver operating curve (AUC), an index of the model accuracy, showed lower value. Based on the relationship between the replicates of the experiments and AUC, the minimum requirements of the replicated experiment was found to be ≥ 10 and ≥12 times for the logistic regression model and neural network model, respectively. The results clearly demonstrated the minimum requirements of experimental replicates for the development of stochastically definite growth/no growth boundary model.