An artificial neural network has been proved to be a sufficient tool for modelling fatigue life of multidirectional composite laminates made of GFRP composite materials and tested under constant amplitude loading patterns. Modelling efficiency of the network was satisfactory for both on- and off-axis coupons’ life, irrespective of the test conditions, i.e., R-ratio that defines the developed stress state on the coupon. Tension–Tension, Compression–Compression and even Tension–Compression loading patterns were investigated and modelling accuracy of the proposed ANN model was validated. The main benefit of this new modelling tool is that only a small portion, in the range of 40–50%, of the experimental data is needed for the whole analysis. Thus, expensive and time consuming tests required by the conventional way for the establishment of S–N curves could be significantly reduced without significant loss of accuracy. We applied the neural network method using experimental data from two different (in nature) material systems and proved that constant life diagrams (CLDs), which are very useful for the design of structures loaded under variable amplitude loading spectra, can be efficiently modelled using a much smaller set of experimental data compared to that needed for the development of CLDs by the conventional way.