aspg-18-4075-1792025491 20261015005131 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2026 18 2 Marwa T. Albayati College of Computing and Informatics, University Tenaga Nasional (UNITEN), Selangor, Malaysia Mohd Ezanee Bin Rusli College of Computing and Informatics, University Tenaga Nasional (UNITEN), Selangor, Malaysia Moamin A. Mahmoud College of Computing and Informatics, University Tenaga Nasional (UNITEN), Selangor, Malaysia Aws A. Abdulsahib Dijla University College, Computer Science Department, Baghdad, Iraq Mohammed F. Alomari Dijla University College, Computer Science Department, Baghdad, Iraq Sallar S. 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mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:none; text-autospace:none; 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;} Microorganisms are commonly found in our daily living environments and play a crucial role in environmental pollution control, disease prevention, and treatment, as well as food and drug production. To fully utilize the diverse functions of microorganisms, their analysis is essential using Intelligent Systems. Traditional analysis methods can be labor- intensive and time-consuming. As a result, image analysis using Intelligent Systems i.e. machine learning or deep learning have been introduced to improve efficiency. Deep learning networks algorithms such as CNN contain a stack of multi-layer, the first layer and the last are the input and output layers, between them are the hidden layers to extract and learn many features in images, recurrent network algorithms (RNN) combined with convolution neural network (CNN), these networks allow to process a series of images to extract the crucial information from images and also these algorithms help to minimize the size of images and reduce the redundancy in microrganisms images According to previous studies, these algorithms are the most used to classify the images of microorganisms. However, the classification of microorganism images presents several challenges these include the need for robust algorithms due to varying application contexts, the presence of insignificant features, along various analysis tasks that need to be addressed. The research summarizes significant advancements that tackle these challenges through deep learning and machine learning methods. Current obstacles, gaps in knowledge, unresolved issues, limitations, and difficulties in classification techniques are also discussed. 2026 341 360 4075 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.180224 https://americaspg.com/journal/18/article/4075 https://americaspg.com/storage/71757010492.pdf