In the realm of the oil and gas industry, the emergence of inorganic scale formations has emerged as a significant concern, profoundly affecting flow assurance and the overall efficiency of oil and gas fields. These precipitates, with their potential to inflict damage on reservoirs, well completions, and surface facilities, present formidable challenges to petroleum engineers, production specialists, and facility managers alike. Despite the existence of numerous commercial computer applications designed to forecast inorganic scale formations with a degree of precision, a major drawback lies in their inherent limitations, which can detrimentally affect the accuracy and efficacy of these predictions. In recent years, there has been a considerable surge in the application of machine learning (ML) within the oil and gas sector. This study aims to delve into the utilization of machine learning algorithms as a fresh and innovative avenue for predicting the occurrence of inorganic scale precipitations specifically within carbonate formations in the oil and gas industry. The methodology employed in this present study revolves around the collection of both input and output data. This dataset encompasses a wide array of variables including pressure, temperature, artificial lifting method, target formation, water ionic composition, water cut, pH levels, total dissolved solids (TDS), and the propensity of each well to precipitate inorganic scale. For the purpose of prediction, a selection of machine learning algorithms has been made. These include Naive Bayes (NA), Neural Network classifier (NN), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The evaluation of these algorithms will be primarily based on metrics related to accuracy and other pertinent classification performance indicators. The outcomes of the model evaluations demonstrate that Gaussian Naive Bayes, Logistic Regression, and Neural Network emerge as the most proficient classifiers regarding prediction accuracy, boasting an impressive score of approximately 90%. The adoption of the suggested model promises numerous advantages. This includes the efficient allocation of well intervention resources, mitigated oil deferment arising from pump failures attributed to scale precipitation, and a reduction in budgetary overruns stemming from unforeseen malfunctions in pumps, valves, or even surface facilities. Consequently, the application of this model not only enhances the operational efficiency of oil and gas production but also contributes to substantial cost savings and improved flow assurance.