Despite the significant progress, the full potential of data-driven and AI methodologies in Sm-Co-based permanent magnet research is yet to be realized. To overcome existing challenges and harness future opportunities, the following prospects are proposed.

1) Enhancing Physical Interpretability of ML Models. Future research should go beyond treating machine learning as a "black-box" predictive tool and place greater emphasis on the physical interpretability of models. On one hand, efforts should be made to actively develop feature descriptors that integrate physical priors and domain knowledge such as magnetism theory and crystal field theory. On the other hand, while continuing to deepen the application of interpretable AI techniques like SISSO and SHAP to quantify the contributions of different features to performance, in-depth research should be conducted to explore the application of new algorithms and optimization strategies such as neural networks. Exploring hybrid models that embed constraints and relationships derived from first-principles calculations and micromagnetic simulations will be crucial for predictions in physical reality.

2) Developing Frameworks for Multi-Source Data and Transfer Learning. The data generated from research on rare-earth permanent magnetic alloys are typical multi-source heterogeneous data, including numerically computed and experimentally measured values, microstructural images, where the microstructure significantly influencing magnetic properties. Future work should leverage deep learning to create unified, machine-readable representations of this diverse data. On this basis, cross-scale transfer learning methods and active learning approaches should be developed to prevent traditional machine learning methods from falling into the traps of overfitting and local optima in small-sample scenarios.

3) Establishing Closed-Loop Inverse Design Systems. The ultimate goal of data-driven approaches is to achieve target-performance-oriented intelligent research and development. Future efforts should focus on constructing an automated intelligent pipeline for inverse design and automated validation. Driven by target performance, this system utilizes conditional generative models to automatically generate candidate compositions and process schemes that meet the requirements. These candidate solutions are then fed into a multi-scale computational chain for simulation validation and optimization. The computational results are fed back as new data into the database and machine learning loops, enabling continuous iteration and optimization of design strategies.

4) Pursuing Data-Assisted Sustainable and Economic Alternatives. By constructing a database incorporating substitution elements, this approach predicts the impact of partially replacing Sm with high-abundance rare-earth elements such as Ce/La on magnetic properties. The goal is to enhance the utilization of high-abundance rare-earth elements while ensuring high performance, thereby developing new cost-effective composition systems. Furthermore, by comprehensively considering factors such as manufacturing processes, raw material costs, and performance merits, novel low-carbon and environmentally friendly rare-earth permanent magnetic alloy systems can be designed.