Pachychoroid disease (PCD) represents a group of disorders characterized by pathological choroidal thickening. The characteristic changes include dilated choroidal vessels in Haller's layer, thinning of the choriocapillaris and Sattler's layer, and retinal pigment epithelium (RPE) abnormalities overlying the pachyvessels. The PCD primarily encompasses uncomplicated pachychoroid (UCP), pachychoroid pigment epitheliopathy (PPE), central serous chorioretinopathy (CSC), pachychoroid neovasculopathy (PNV), and polypoidal choroidal vasculopathy (PCV). Traditional fundus examination is limited to the posterior pole in single-frame imaging, making it challenging to comprehensively evaluate the extent of lesions. Wide-field imaging technology has overcome this limitation, with its imaging range covering from the posterior pole to the ampulla of vortex veins at the equator (approximately 60-100°), while ultra-wide-field imaging can extend from the posterior pole to the pars plana (approximately 110-220°). This technological advancement has not only expanded the observation range of PCD fundus lesions but also enhanced the assessment capabilities of choroidal structure and function, providing new perspectives for investigating PCD pathogenesis. In recent years, deep learning-based artificial intelligence technology has achieved significant breakthroughs in PCD-assisted diagnosis, demonstrating excellent capability in identifying and classifying PCD-related diseases. This has contributed to significantly improving diagnostic efficiency in primary healthcare institutions and optimizing medical resource allocation. This review summarizes the advances in wide-field fundus imaging technologies for the assessment and diagnosis of PCD.