<p>Multispectral compatible camouflage represents a cutting-edge field in modern military stealth technology. In recent years, machine learning (ML) has emerged as a method for developing new materials, leveraging its advantages in data processing, nonlinear mapping, and inverse design. This review systematically summarizes the progress in ML-driven multispectral camouflage. First, it provides the ML paradigms commonly adopted in the study of multispectral camouflage and stealth materials. It then explores traditional camouflage and stealth material design, followed by a detailed discussion of ML applications in the design of single-band stealth materials (visible, infrared, radar) as well as multiband compatible stealth materials. A key focus is placed on clarifying the role of ML in multispectral compatible design: it serves as an efficient Pareto front navigator, capable of tackling complex high-dimensional multi-objective optimization problems and achieving globally balanced performance while respecting the inherent physical trade-offs among different spectral bands. Finally, it identifies current challenges such as data scarcity and model interpretability, and suggests that integrating active learning, generative models, and physics-informed ML will be crucial for developing next-generation adaptive and intelligent stealth materials.</p>