Glaucoma causes permanent vision loss. Glaucoma is estimated to affect over 80 million public worldwide in the year 2030. Individuals, physicians, and other health-care professionals realize absolutely nothing regarding the way data inspection and processing work, but contemporary ML (machine learning) models are mostly used for glaucoma prediction. Options for enhancing customer trust along the way are provided by IML (interpretable machine learning) and XAI (explainable artificial intelligence). The chapter discusses the XAI and IML methods for analyzing glaucoma hypotheses and findings. XAI relies heavily on the ANFIS (adaptive neuro-fuzzy inference system) and PDA (pixel density analysis) to provide consistent elucidations for glaucoma forecasts derived from both high-quality audio and image data. To make meaning of data, IML employs a technique called SP-LIME (semi-private selection adjacent interpretable model-agonistic clarification). This is used for the decoding of SNN findings. The implementation consequences demonstrate that XAI and IML techniques provide patients and physicians with convincing and unambiguous recommendations using two specific, readily accessible datasets: understanding scan fundus images and glaucoma case medical information.