Machine learning (ML) models are increasingly used in glaciology to replace computationally expensive model components or emulate specific processes. These approaches, however, are commonly developed as standalone tools or within application specific software and cannot be readily deployed within established ice-sheet modeling frameworks. We present ISSM-ML, a modular framework for integrating externally trained, Python-based ML models into the Ice-sheet and Sea-level System Model (ISSM). Model development and training remain within the Python ecosystem, while ISSM invokes selected ML modules at runtime and retains control of model initialization, numerical solvers, physical analyses, time stepping, and other procedures in its original workflow. We demonstrate the framework using a multilayer perceptron to replace the nonlinear velocity-dependent term in the Weertman basal-friction law. Two training strategies are evaluated, one using conventional scaling and the other using distribution-aware preprocessing. When the scaling-only model is coupled to ISSM, the stress balance solver fails to converge. The distribution-aware model, in contrast, allows ISSM to converge reliably throughout the transient simulation, with a good agreement with the analytical-friction reference. These results demonstrate that offline prediction accuracy alone is not sufficient for evaluating an ML component intended for numerical-model coupling. Their behavior must also be assessed within the simulation environment in which they operate, including convergence, stability, and feedback with evolving model states.