Failure assessment diagrams (FADs) are essential engineering tools for evaluating the structural integrity of components. However, their widespread application can be limited by complexity and computational expense. This study presents a novel machine learning-based approach to streamline FAD analysis, offering accuracy and efficiency while overcoming these limitations. The approach integrates numerical contour integral-based FADs with artificial neural networks (ANNs). To ensure reliable material modeling for the Finite Element Analysis (FEA) used to generate J-integral based FADs that train the ANNs, careful experimental and numerical procedures were employed. This involved uniaxial tensile tests, an iterative method for obtaining precise true stress–strain curves, and a Ramberg–Osgood material model for accurate material behavior representation. The ANNs themselves not only analyze large datasets to generate precise FAD envelopes but also predict limit loads and the Φ parameter, incorporating the effect of residual stress on the FAD methodology. To verify and test the proposed method, hypothetical fitness-for-service assessment cases were conducted, incorporating experimental residual stress measurements from split-ring tests on P110 and L80 pipes. These assessments were compared to both traditional FAD methods and computationally intensive FEA-based FADs. Results demonstrate a closer agreement with FEA-based calculations than traditional methods provided in engineering standards. Ultimately, this work provides a rather innovative and adaptable approach for structural integrity evaluations and critical engineering assessments through the proposal of an ANN enhanced FAD approach, simplifying these calculations while maintaining high fidelity.