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[Display omitted] • Metals are crucial element in terms of biological, environmental, and/or industrial point of view. • Discussed greater than 150 rhodamine probes considering structure, ions, medium & detection limit. • Rhodamine probes for fabrication of AI-ML based model as lab-at-home. • This review is a reference with a wide range of dataset and AI-ML programming for future modelling. Rhodamine based probes are well documented in literature as a host for detection of several guest analytes including metal ions. Metals are crucial elements in terms of biological, environmental, and/or industrial point of view- an optimum value of it within the media is thus necessary to maintain. Deficiency could lead to multiple disorders whereas excess could end up by malfunctioning the system. The detection of metal ions in various concentrations by rhodamine probes followed multiple mechanisms, one common pathway is opening the spirolactam ring within the rhodamine scaffold which leads to colorimetric and fluorometric signals. Rhodamine itself is less emissive and less colorful when the spirolactam ring is present within the framework and would become strongly emissive with versatile coloring range (red, orange or purple etc) once the ring is opened. To understand how the sensing occurs by the rhodamine probes in presence of metal ions, we have discussed more than 150 important rhodamine probes with almost all possible metal ions, pointing out several issues i.e., role of solvent, role of rhodamine moieties itself, role of side arm etc. Although scanty in daily life, we believe rhodamine probes could be an easy-instant-economic strategy for PoCT application to diagnose multiple diseases and could be operational without any trained personnel to establish lab-at-home. We want to use the available dataset based on rhodamine probes for fabrication of artificial intelligence and machine learning (AI-ML) based model that could be useful in future. We believe the review would support the researchers in this field as a ready reference by providing a wide range of datasets (structure, ions detected, medium, detection limit etc) along with fundamental AI-ML programming for future modelling purposes as AI-ML based models are being used by several researchers, towards protein engineering, cell penetrating peptide designing etc. [ABSTRACT FROM AUTHOR]