The production of alternative proteins to meet the demand of a growing population has accelerated the growth of the market for edible insects. Black fly soldier (BFS) larvae (Hermetia illucens L.) have been widely studied globally due to their high content of fat, protein, and minerals, being mainly used for animal feed. Chemical analysis for determination of its composition is time consuming and laborious. In this work, we have developed predictive models based on Near Infrared Hyperspectral Imaging (NIR-HSI), Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) to estimate the total protein content in single and intact BFS larvae. A variable selection step by interval PLS (iPLS) and genetic algorithms (GA) was implemented to improve regression model performance. In addition, BFS larvae hyperspectral images were explored using Principal Component Analysis (PCA), whose results showed the distribution of the different chemical compounds in the larvae. The PLSR and SVMR models reached RMSEP values of 1.57–1.66% and RPD values of 2.0–2.5, indicating a good approximate prediction capacity (% protein range 25.5–43.5%). Variables selected by iPLS obtained better regression models than variables selected by GA, based on the lower absolute error. Chemical maps displayed the heterogeneous protein distribution in single larvae and a batch of larvae. This manuscript demonstrates that NIR-HSI and chemometrics can be implemented as a fast screening method to estimate protein content in single BFS larvae.