In this study, we conduct a detailed evaluation of machine learning and multimodal learning approaches in two distinct areas: a standard medical imaging benchmark and a novel material sciences benchmark. We utilize the CheXpert chest x-ray dataset for medical imaging and introduce a newly created Fluoropolymer Atomic Force Microscopy (AFM) dataset for material sciences. Both datasets are enhanced with additional images and binary metadata, encoded as one-hot vectors. We tested both pretrained and non-pretrained Convolutional Neural Network (CNN) models, such as ResNet50, ResNet101, DenseNet121, InceptionV3, and Xception, across different combinations of image and metadata inputs. Our results reveal that integrating multimodal data, including simple binary metadata, significantly enhances classification accuracy compared to conventional unimodal approaches or advanced MADDi models. This indicates the efficacy of multimodal learning in enriching data representation and boosting image classification performance. Notably, Xception models showed exceptional performance in CheXpert tests, and most models improved crystal structure predictions in AFM datasets. These insights set a new benchmark for performance and underscore the potential of multimodal learning in data-intensive applied science research.