Remote sensing is of great importance for analyzing and studying various phenomena occurrence and development on Earth. Today is possible to extract features specific to various fields of application with the application of modern machine learning techniques, such as Convolutional Neural Networks (CNN) on MultiSpectral Images (MSI). This systematic review examines the application of 1D-, 2D-, 3D-, and 4D-CNNs to MSI, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review addresses three Research Questions (RQ): RQ1: “In which application domains different CNN models have been successfully applied for processing MSI data?”, RQ2: “What are the commonly utilized MSI datasets for training CNN models in the context of processing multispectral satellite imagery?”, and RQ3: “How does the degree of CNN complexity impact the performance of classification, regression or segmentation tasks for multispectral satellite imagery?”. Publications are selected from three databases, Web of Science, IEEE Xplore, and Scopus. Based on the obtained results, the main conclusions are: (1) The majority of studies are applied in the field of agriculture and are using Sentinel-2 satellite data; (2) Publications implementing 1D-, 2D-, and 3D-CNNs mostly utilize classification. For 4D-CNN, there are limited number of studies, and all of them use segmentation; (3) This study shows that 2D-CNNs prevail in all application domains, but 3D-CNNs prove to be better for spatio-temporal pattern recognition, more specifically in agricultural and environmental monitoring applications. 1D-CNNs are less common compared to 2D-CNNs and 3D-CNNs, but they show good performance in spectral analysis tasks. 4D-CNNs are more complex and still underutilized, but they have potential for complex data analysis. More details about metrics according to each CNN are provided in the text and supplementary files, offering a comprehensive overview of the evaluation metrics for each type of machine learning technique applied.