High-dimensional remote-sensing data used in wildfire modeling poses significant analytical challenges due to its complexity. Traditional machine learning methods often struggle with such data, leading to loss of crucial multi-dimensional information. This study used a tensor-based approach that combines Tensor Train (TT) and Canonical Polyadic (CP) decompositions with the Dual Structure-preserving Kernel (DuSK) to efficiently model and analyze high-dimensional wildfire data. By transforming data tensors into TT-CP format, we maintain essential multi-modal interactions while reducing computational demands. The DuSK kernel facilitates accurate computations across tensor modes using Gaussian Radial Basis Functions. Implementing support tensor train machine with dual kernel (TT-DK), we achieve better accuracy and efficiency compared to conventional SVMs that rely on data flattening, when handling complex, multi-dimensional remote-sensing data. The proposed framework not only improves the accuracy of the wildfire model but also optimizes computational resources, making it suitable for real-time and large-scale environmental monitoring applications. This approach provides a valuable tool for early wildfire intervention and has potential applications in other areas of remote sensing data analysis. This study provides a robust and scalable solution for analyzing complex datasets, paving the way for enhanced decision-making in wildfire management.