In systems operating with extremely large antenna arrays and high-frequency signaling, multiple users often reside in the radiative near-field, and accurate localization becomes essential. Unlike conventional far-field systems that rely solely on direction of arrival (DoA) estimation, near-field localization exploits spherical wavefront propagation to recover both DoA and range information. While subspace-based methods, such as MUltiple SIgnal Classification (MUSIC) and its extensions, offer high resolution and interpretability for near-field localization, their performance is significantly impacted by model assumptions, including non-coherent sources, well-calibrated arrays, and a sufficient number of snapshots. To address these limitations, this work proposes artificial intelligence (AI)-aided subspace methods for near-field localization that enhance robustness to real-world challenges. Specifically, we introduce NF-SubspaceNet, a deep learning-augmented 2D MUSIC algorithm that learns a surrogate covariance matrix to improve localization under challenging conditions, and DCD-MUSIC, a cascaded AI-aided approach that decouples angle and range estimation to reduce computational complexity. We further develop a novel model-order-aware training method to accurately estimate the number of sources, that is combined with casting of near-field subspace methods as AI models for learning. Extensive simulations demonstrate that the proposed methods outperform classical and existing deep-learning-based localization techniques, providing robust near-field localization even under coherent sources, miscalibrations, and few snapshots.