The integration of multi-omics data has become crucial in understanding the complexity of biological systems and disease mechanisms. However, the high dimensionality and heterogeneity of such data present significant analytical challenges. This review investigates the emerging approach of transforming multi-omics non-image data into image formats to facilitate the application of advanced deep learning techniques for disease classification and biomarker discovery. This article presents a scoping review of studies published between 2013 and 2024, focusing on techniques that convert multi-omics data into images. Various transformation methods, including t-SNE, kernel PCA, UMAP, FFT, and treemaps, were examined alongside deep learning models such as convolutional neural networks, autoencoders, support vector machines, graph convolutional networks, and graph neural networks. The transformation of omics data into image formats enables effective feature extraction and classification, with reported accuracies ranging from 75% to 99% across various studies. CNN-based models, in particular, demonstrated superior performance in integrating complex molecular interactions. Despite these advances, challenges such as overfitting, limited generalizability, and interpretability persist, especially given the diversity and complexity of multi-omics datasets. Finally, the transforming multi-omics data into images represents a promising direction in biomedical research, facilitating more profound insights into disease mechanisms and improving predictive modeling. Addressing current limitations through improved model interpretability, robust transformation methods, and larger, more diverse datasets will be essential for realizing the full potential of this approach in precision medicine.