Material defects govern the performance limits, degradation pathways, and service lifetimes of functional and structural materials, yet their predictive modeling remains difficult because local electronic reconstruction, long-range interactions, rare kinetic events, and finite-temperature effects are coupled across scales. First-principles calculations provide reliable descriptions of defect cores and charge states but are limited by system size, time scale, and sampling cost, whereas empirical molecular dynamics (MD) can access larger systems but often lacks transferability in reconstructed or chemically complex defect environments. This review examines machine learning (ML) for defect modeling when model choice, training data, and validation criteria are matched to the physical problem. We discuss machine-learning interatomic potentials (MLIPs), sparse Gaussian-process and committee-machine models, active-learning workflows, high-throughput defect screening, and ML-assisted multiscale simulations that connect atomistic calculations with longer-timescale modeling. Particular attention is given to the distinct requirements of point defects, dislocations, surfaces, grain boundaries, and interfaces, including configurational sampling, long-range interactions, charge response, and uncertainty control. Representative applications in semiconductors, halide perovskites, solid electrolytes, battery cathodes, irradiation damage, and catalytic nanoparticles show where ML expands the feasible scientific questions and where first-principles benchmarks or experimental validation remain essential. Reliable ML-assisted defect modeling ultimately depends on validating the physical quantities that control the relevant defect mechanism, rather than relying on aggregate model accuracy alone.