Journal of Applied Chemical Science International

Supported lipid layers are established biomimetic platforms for examining membrane organisation, phase behaviour, interfacial properties, and mechanically induced changes under controlled experimental conditions. Atomic force microscopy (AFM) can provide nanoscale topographical and force-response information; however, conventional interpretation often depends on a limited set of summary descriptors that may not adequately represent spatial heterogeneity, defects, multimodal mechanical populations, or complex force-curve profiles. This Method Article presents a structured framework for machine-learning-assisted analysis of AFM-derived height maps, force-distance or force-indentation curves, roughness measures, stiffness-related variables, adhesion signals, and heterogeneity features. The proposed workflow addresses data provenance, sample hierarchy, image and force-curve preprocessing, feature extraction, grouped data partitioning, supervised and unsupervised learning, quality control, explainable-AI assessment, uncertainty analysis, and reproducibility reporting. It requires independent lipid preparations or experimental batches to serve as the principal grouping units, thereby preventing leakage caused by distributing nested pixels, patches, maps, or curves across training and test partitions. The framework also specifies reporting requirements for model evaluation, calibration, permutation testing, sample-level error analysis, and external validation. As no original, fully documented AFM dataset or executable analysis is provided, the article does not claim validated predictive performance. Instead, it defines the methodological evidence required for future transparent and independently reproducible implementation.