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The tools to predict the growth of bacteria over the time is essential to maintain the process stability in bio processes. Currently, no such tools are used in industries or laboratories. In this paper, an approach based on Finite Impulse Response (FIR) type multi layer perceptron aritficial neural network model is proposed. The neural network model was developed using data collected from laboratory work. As amount of 75% growth of bacteria (S. Aureus, B. Cereus and S. Typhimurium) which is inhibited by lemon basil waste extract, over the time data are used to train Artificial Neural Network (ANN), and the rest of the data are used to validate the model. ANN has been model the growth of S. Aureus, B. Cereus and S. Typhimurium which is inhibited by lemon basil waste extract over the time. It was observed that the Mean Square Error (MSE) during training is 0.087 and validation is 0.147. It proving that the modeling approach employed in this research is suitable to capture the nonlinear characteristics of the bacteria growth.