In providing spatial context to molecular expression, spatial omics technologies have profound implications for disease diagnosis, outcome prediction, and the development of novel therapeutics. However, the inherent limitations and complexities of spatial omics data restrict their interpretability and complicate their integration into clinical settings. For instance, sequencing-based spatial transcriptomics data lack single-cell resolution, hindering the precise localization of specific cell types. Additionally, while spatial omics technologies can generate multiple omics and imaging modalities from a single tissue section, effectively integrating these diverse modalities remains a complex task. Moreover, the vast amount of data produced by imaging-based spatial transcriptomics experiments, which can encompass millions of cells, renders traditional spatial omics analysis methods ineffective. This dissertation addresses these challenges by harnessing machine learning to enhance the analysis and interpretation of spatial omics data. First, we developed a cell-type deconvolution method for sequencing-based spatial transcriptomics data that combines gene expression and histology imaging data to accurately estimate spatial distributions of specific cell types. Second, we developed a multi-modal feature extraction and spatial clustering algorithm that can integrate data from any number of omics and imaging modalities. Lastly, we developed a workflow for analyzing data from MERFISH experiments consisting of 16 million cells across eight cortical areas and four developmental stages from the human fetal cortex. Each of these projects aims to bolster the power and interpretability of spatial omics data, thereby accelerating the discovery of novel biological insights and facilitating the incorporation of spatial omics into clinical practice.