<jats:p>Endometrial cancer (EC) is the most common malignancy of the female reproductive tract worldwide. Although comprehensive genomic analyses of EC have already uncovered many recurrent genetic alterations and deregulated signaling pathways, its disease model has been limited in quantity and quality. Here, we review the current status of genetic models for EC in mice, which have been developed in two distinct ways at the level of organisms and cells. Accordingly, we first describe the <jats:italic>in vivo</jats:italic> model using genetic engineering. This approach has been applied to only a subset of genes, with a primary focus on <jats:italic>Pten</jats:italic> inactivation, given that <jats:italic>PTEN</jats:italic> is the most frequently altered gene in human EC. In these models, the tissue specificity in genetic engineering determined by the <jats:italic>Cre</jats:italic> transgenic line has been insufficient. Consequently, the molecular mechanisms underlying EC development remain poorly understood, and preclinical models are still limited in number. Recently, refined <jats:italic>Cre</jats:italic> transgenic mice have been created to address this issue. With highly specific gene recombination in the endometrial cell lineage, acceptable <jats:italic>in vivo</jats:italic> modeling of EC development is warranted using these <jats:italic>Cre</jats:italic> lines. Second, we illustrate an emerging cell-based model. This hybrid approach comprises <jats:italic>ex vivo</jats:italic> genetic engineering of organoids and <jats:italic>in vivo</jats:italic> tumor development in immunocompromised mice. Although only a few successful cases have been reported as proof of concept, this approach allows quick and comprehensive analysis, ensuring a high potential for reconstituting carcinogenesis. Hence, <jats:italic>ex vivo/in vivo</jats:italic> hybrid modeling of EC development and its comparison with corresponding <jats:italic>in vivo</jats:italic> models may dramatically accelerate EC research. Finally, we provide perspectives on future directions of EC modeling.</jats:p>