The predictive capabilities of artificial intelligence for predicting protein yield from larval biomass present valuable advancements for sustainable insect farming, an increasingly relevant alternative protein source. This study develops a neural network model to predict protein conversion efficiency based on the nutritional composition of larval feed. The model utilizes a structured two-layer neural network with four neurons in each hidden layer and one output neuron, employing logistic sigmoid functions in the hidden layers and a linear function in the output layer. Training is performed via Bayesian regularization backpropagation to minimize mean squared error, resulting in a high regression coefficient (R = 0.9973) and a low mean-squared error (MSE = 0.0072401), confirming the precision of the model in estimating protein yields. This AI-driven approach serves as a robust tool for predicting larval protein yields, enhancing resource efficiency and promoting sustainability in insect-based protein production.