This study explores the potential of improving vision transformer, MLP-Mixer, and global filter network for cloud detection using Sentinel-2 images. A mechanism is proposed to embed K-means clustering into frameworks for cloud detection (KET-CD) to enhance the performance. A comparative analysis of these architectures for multiclass cloud detection is conducted by leveraging two standard datasets: IndiaS2 and KappaSet. Our results demonstrate that the KET-CD methods achieve remarkable accuracy by outperforming the baseline architectures and state-of-the-art cloud detection methods (Fmask and Sen2Cor), exceeding a 0.5 mean intersection over union score. By incorporating cluster label embedding, KET-CD significantly improves the ability of these models to detect clouds and their shadows effectively in complex regions. These findings validate the importance of transformer-inspired architectures in processing large-scale satellite data for cloud detection tasks and highlight KET-CD as a promising solution for accurate cloud detection in remote sensing applications.