Airborne spectral imaging，including hyperspectral imaging （HSI） and multispectral imaging （MSI） data from unmanned aerial vehicles （UAVs） and manned aircraft，has become a cornerstone of modern remote sensing. Compared to satellites，airborne platforms offer ultra-high spatial resolution，operational flexibility，short revisit cycles，and customizable sensors. Because of these advantages，they have become indispensable for precision agriculture，environmental monitoring，military reconnaissance，urban planning，geological exploration，and cultural heritage preservation. However，airborne spectral data present unique challenges，including high dimensionality （especially for HSI with hundreds of bands），strong spectral-spatial correlations，complex noise，geometric distortions from platform motion，variable illumination，and the mixed-pixel problem. Traditional methods （e.g.，SVM，random forests，and physical models） struggle to fully exploit these data due to limited feature representation，the curse of dimensionality，and poor generalization across sensors or flight conditions. As such，this paper systematically reviews intelligent processing techniques for airborne spectral images，focusing on deep learning and advanced machine learning. First，we highlight the complementary roles of HSI and MSI，stating that HSI provides rich continuous spectra for fine material identification and sub-pixel analysis，while MSI offers higher signal-to-noise ratio，lower data volume，and better real-time performance. Their synergistic use，often in a layered strategy （MSI-based coarse detection followed by HSI-based fine identification），is a key trend. Next，we discuss in detail the preprocessing pipeline—radiometric calibration，atmospheric correction，geometric registration，denoising，and resolution enhancement—noting the shift from purely physical models to hybrid data-driven and physics-informed deep learning. The core of the review covers six intelligent processing tasks. For spectral-spatial feature extraction，we trace the evolution from 2D/3D CNNs and hybrid CNN-Transformer models to state-space models like Mamba，which have been shown to achieve linear complexity while capturing long-range spectral dependencies，balancing accuracy and efficiency for real-time scenarios. For classification and segmentation，we discuss supervised，semisupervised，few-shot，and self-supervised methods （e.g.，HybridSN，Spectral-Former，and masked spectral modeling），as well as lightweight networks （MobileNet and EfficientNet） and transfer learning dominate for MSI，with cross-domain few-shot learning addressing label scarcity. For target detection，HSI exploits subtle spectral differences for camouflaged targets and chemical agents using anomaly detection or Transformer-based detectors （e.g.，SpecDETR），while MSI leverages YOLO and Faster R-CNN for real-time object detection. In addition，multimodal fusion （visible，thermal，spectral） further improves robustness. For change detection （CD），Siamese networks，Transformers （BIT and ChangeFormer），and Mamba hybrids are applied to multitemporal images，overcoming specific challenges in distinguishing real changes from illumination/atmospheric variations and incorporating physical constraints. For spectral unmixing，autoencoders learn nonlinear mixtures but lack physical constraints （nonnegativity，sum-to-one，and energy conservation），with recent efforts embedding linear mixture or radiative transfer models into deep networks for better interpretability. For multisource fusion，we cover heterogeneous fusion （HIS + LiDAR and HIS + SAR） using multimodal Transformers or GNNs，as well as spectral-spectral fusion （HIS + MSI） to generate high-resolution hyperspectral data，outlining four collaborative strategies： HSI-guided band selection，MSI-assisted dimensionality reduction，layered cascaded processing，and end-to-end joint modeling. A dedicated section addresses real-time processing and lightweight deployment on airborne edge devices （e.g.，NVIDIA Jetson）. Furthermore，we review model compression （pruning，quantization，knowledge distillation，and neural architecture search） and evaluate backbones （MobileNet，HybridSN，Mamba，and efficient Transformers） for onboard inference. While real-time HSI classification and MSI detection have been demonstrated，real-time unmixing and CD remain challenging. The paper surveys typical applications，emphasizing HSI/MSI complementarity，including precision agriculture （early disease detection，nutrient/water stress，and fruit counting），environmental and disaster monitoring （oil spills，red tides，forest fires，air pollution，and mining），military reconnaissance （camouflage target detection and chemical agents），urban and infrastructure （land cover classification，building CD，traffic，and infrastructure health），geological exploration （mineral mapping，alteration zones，and oil/gas microseepage），and archaeology （subsurface relics，surface disease analysis）. Next，we identify five core challenges，namely scarcity of high-quality labeled data，insufficient model generalization，real-time constraints，and lack of interpretability （black-box models ignoring physical mechanisms），and difficulties in air-space-ground collaboration （heterogeneous data，limited bandwidth，and cross-platform transfer）. Finally，we outline the following directions for future works： foundation models with self-supervised learning for spectral-spatial representation； lightweight edge intelligence via hardware-software co-design； physics-informed deep models embedding radiative transfer or mixture constraints； robust and trustworthy AI with adversarial defense and uncertainty quantification； and integrated air-space-ground collaborative observation systems for hierarchical and intelligent remote sensing. In summary，this review provides a systematic reference for researchers and engineers advancing airborne spectral image intelligent processing. The datasets mentioned in this paper have been compiled and are available at https://github.com/zhaoxb2025/Airb-spe-Img.