aspg-3-4045-1791419317 20261008002837 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2026 21 2 Yaman Huthaifa Saeed Aldewachi Computer Center, University of Mosul, Mosul, Iraq 16.00 Normal false false false TR X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> Name="index 1"/> Name="index 2"/> Name="index 3"/> Name="index 4"/> Name="index 5"/> Name="index 6"/> Name="index 7"/> Name="index 8"/> 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Light Accent 5"/> Name="Grid Table 6 Colorful Accent 5"/> Name="Grid Table 7 Colorful Accent 5"/> Name="Grid Table 1 Light Accent 6"/> Name="Grid Table 6 Colorful Accent 6"/> Name="Grid Table 7 Colorful Accent 6"/> Name="List Table 1 Light Accent 1"/> Name="List Table 6 Colorful Accent 1"/> Name="List Table 7 Colorful Accent 1"/> Name="List Table 1 Light Accent 2"/> Name="List Table 6 Colorful Accent 2"/> Name="List Table 7 Colorful Accent 2"/> Name="List Table 1 Light Accent 3"/> Name="List Table 6 Colorful Accent 3"/> Name="List Table 7 Colorful Accent 3"/> Name="List Table 1 Light Accent 4"/> Name="List Table 6 Colorful Accent 4"/> Name="List Table 7 Colorful Accent 4"/> Name="List Table 1 Light Accent 5"/> Name="List Table 6 Colorful Accent 5"/> Name="List Table 7 Colorful Accent 5"/> Name="List Table 1 Light Accent 6"/> Name="List Table 6 Colorful Accent 6"/> Name="List Table 7 Colorful Accent 6"/> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman",serif; mso-ansi-language:TR;} E-government implementation in developing countries faces obstacles and challenges far beyond being a simple technology., By interviewing citizens through its enhanced PEST (Political, Economic, Social, and Technological) analysis and artificial intelligence algorithms, this study systematically evaluates the experiments of Iraq to accommodate e-government service. 1,081 Iraqi citizens were surveyed using mixed methods to quantify their public acceptance and willingness of e-government services, as well as identifying the obstacles. Our investigation finds that data security (mean = 3.59-3.80), the political situation, economic distress, a lack of enthusiasm for change in society, and shortfalls of technological infrastructure are all serious challenges at present. The research used advanced statistical methods, including correlation analysis (0.634 technology-trust relationship), regression models (R ^ 2 = 0.542), factor analysis (KMO = 0.891), and Multi-Layer Perceptron (MLP) neural network algorithms achieved 89.8% prediction accuracy for e-government acceptance. The AI algorithm supported the conclusions drawn from statistical tests, with Technology Readiness and Security Perception rising up as two most significant predictors (23.4% importance for Technology Readiness and 19.8% importance for Security Perception). The findings also propose a novel methodological framework that integrates traditional statistical analysis with machine learning capabilities, rendering concrete recommendations to developing country policy makers. The study's findings imply that successful e-government implementation requires a holistic approach that factors in political, economic, social and technological aspects together. The composite PEST index score of 0.826 smells widespread resistance on the ground, although AI predictive model greatly facilitates forecasting for future e-government initiatives. 2026 22 41 4045 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.210202 https://www.americaspg.com/journal/3/article/4045 https://www.americaspg.com/storage/41756575870.pdf