aspg-18-4154-1792025503 20261015005143 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 17 1 Zaid Derea College of Computer Science and Information Technology, Wasit University, Wasit 52001, Iraq Ammar Kazm Department of Computer, College of Education for Pure Sciences, Wasit University. Iraq Manar Bashar Mortatha Department of Computer, College of Education for Pure Sciences, Wasit University. Iraq Oday Ali Hassen Department of Computer, College of Education for Pure Sciences, Wasit University. Iraq; Ministry of Education, Wasit Education Directorate, Wasit, Iraq Esraa Saleh Alomari Department of Computer, College of Education for Pure Sciences, Wasit University. 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mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:TR;} Rapid spread of Corona virus 2019 (COVID-19) is predictable to create high contact on healthcare organization. Early detection of this disease is required to make precise treatment that further helps to increase the survival rate of humans. However, detecting the COVID-19 at beginning stage is one of a major challenge in the world because of rapid disease spread. Various existing methods are developed to detect the disease, but generating accurate result at the beginning stage still poses a complex task in the medical research community. Hence, an effective mechanism is modeled in this research to predict the pandemic at early with the time-series data using proposed Water Poor and Rich optimization-based Deep Recurrent Neural network (WPRO-based Deep RNN). Accordingly, proposed method is highly effective in generating the most appropriate results through deep learning classifier based on the high dimension features. However, the high dimensional data is generated through the data augmentation process by employing oversampling technique. The proposed method is more robust and increases the efficiency of the optimization algorithm by attaining global convergence results based on the fitness measure. Accordingly, the technical features of time series data to improve effectiveness of developed model. However, the proposed WPRO-based Deep RNN produced minimum Root Mean Square Error (RMSE) as well as MSE values of 0.4 and 0.1714 for confirmed cases, and obtained lesser MSE and RMSE values of 0.1887 and 0.433 for the cases of death. Moreover, proposed model achieved minimal RMSE and MSE of 0.447 and 0.1901 for the recovered cases. 2025 342 359 4154 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170124 https://americaspg.com/journal/18/article/4154 https://americaspg.com/storage/51760713436.pdf