Identifying Majorana zero modes in semiconductor-superconductor nanowires requires ultralow temperature transport measurements in dilution refrigerators, making device screening slow and resource intensive. Here, we investigate whether high-temperature conductance data can be used to infer low-temperature Majorana nanowire properties before committing devices to dilution-refrigerator characterization. We generate paired high- and low-temperature conductance simulations for disordered Majorana nanowires and train neural networks to perform two related tasks. First, we use a shifted window U-net transformer diffusion-inspired architecture to reconstruct low-temperature conductance from thermally broadened high-temperature measurements, achieving high-fidelity recovery with $R^2\approx 0.95$ for local conductance and $R^2\approx 0.91$ for nonlocal conductance. Second, we train a video vision transformer-based network to predict the low-temperature topological visibility directly from high-temperature conductance, obtaining $R^2\approx 0.80$. These results demonstrate that machine-learning models can recover and infer low-temperature Majorana features from experimentally easier high-temperature data, providing a practical route for rejecting poor devices early thus avoiding slow and resource-intensive dilution refrigeration for nonpromising devices. This high-temperature screening approach could substantially accelerate the experimental feedback loop for Majorana nanowire device development.

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• Majorana bound states
• Majorana zero modes
• Topological superconductors
• Artificial neural networks
• Machine learning