This study includes an artificial neural network (ANN) analysis of irreversibility in Johnson–Segalman nanofluid flow through a peristaltic channel under the influence of motile microorganisms, viscous dissipation, and slip effects. The nonlinear partial differential equations are transformed into ordinary differential equations by applying the lubrication approximation and Debye–Hückel transformations with the help of suitable dimensionless variables. The resultant dimensionless ordinary differential equations are solved analytically using the homotopy perturbation method (HPM) by linearizing and assuming a series solution. The linear subproblems from HPM are solved successively to find the symbolic series solution in MATLAB by utilizing the dsolve command. The symbolic solutions for velocity, temperature, concentration, and bioconvection are plotted against different physical parameters to visualize their behavior and profiles. Moreover, data for velocity, thermal, concentration, and bioconvection profiles are extracted to train the ANN model. The ANN model is trained in Python using TensorFlow version 2.17.0., and it consists of one input layer, two hidden layers (each with 64 neurons), and one output layer. The ReLU activation function is used in the hidden layers, and the Adam optimizer is employed in our model. Performance metrics such as mean square error (MSE), regression (R2), error histogram, gradient, and relative error, and absolute error are computed to monitor the performance of the ANN model. Results show that the ANN demonstrates promising accuracy in predicting and learning the velocity, thermal, concentration, and momentum profiles. The findings indicate that the magnetic field and Prandtl number significantly influence the thermal profile, while the velocity profile is affected by the Darcy parameter. This work has potential applications in biomedical engineering, particularly in the design of microfluidic devices for targeted drug delivery, and also holds relevance for environmental engineering.