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This study presents the computer aided design (CAD) of type-I quantum-cascade lasers (QCLs) based on Artificial Neural Networks (ANNs). QCLs have critical quantities named modal gain, differential refractive index change and the linewidth enhancement factor (LEF, Â¿ parameter). Each of these quantities requires lengthy mathematical calculations using different theories and assumptions. The single model is based Multi-layer Perceptrons (MLPs) approach which decreases the computational time with accurate values. MLPs are trained and tested with different learning algorithms and different network configurations in order to get an accurate model. The results are in very good agreement with the previously published results.