Cloud computing is a technology that allows users to access and store data, applications, and services over the internet. It provides on-demand availability of computing resources such as storage, processing power, and networking through cloud service providers. Cloud computing offers several advantages, including scalability, cost efficiency, flexibility, and remote accessibility. Predictive analytics in the cloud refers to the use of advanced data analysis techniques, such as machine learning and artificial intelligence, to forecast future trends and behaviors using data stored and processed on cloud platforms. Many researchers carried out their research on cloud data services. But, the accurate and time efficient prediction was major issues. In order to address these issues, an Interquartile Gamma generalized Regressive Agenetic AI (IGRAAI) model is developed for performing the efficient data classification in cloud environment. IGRAAI Model uses artificial intelligence concepts for performing efficient data classification in cloud environment. The proposed IGRAAI model performed four essential processes, namely data collection, data pre-processing, feature selection and data classification. Initially in IGRAAI Model, IoT sensors are used to collect the data points from different location. After that, IGRAAI Model performs the data preprocessing which includes missing data handling and outlier’s detection from input database. Followed by, Gamma Generalized linear Regression is employed for feature Selection in IGRAAI model to choose the relevant features from input database. Finally, Soergel indexive Agentic AI concept is used in IGRAAI Model for efficient data classification in cloud environment. By this way, an efficient data prediction is carried out in cloud environment. Experimental analysis is carried out with the performance metrics like prediction accuracy, precision, recall, RMSE and prediction time with respect to number of data samples. The quantitatively analysis results demonstrate that the proposed IGRAAI model attains superior accuracy in prediction while requiring less computational time as well as error rate than traditional methods.