Awareness, short-term memory, and long-term memory are interrelated cognitive abilities that influence orthographic acquisition under Individual Differences. Connectionists ignore the role of biological grammar in language acquisition and consider external inputs or interventions as factors that shape abstract grammar through network mapping architecture. As new intervention methods, the use of computers in connectionist modeling of language acquisition has increasingly been developed through Artificial Neural Networks. New optimized and accurate computational models have not been applied to investigate foreign language orthography acquisition. The purpose of the present study is to use Multi-Layer Perceptron Neural Network-Gray Wolf Optimizer computational model to simulate and evaluate individualized cognitive abilities including orthographic awareness, orthographic short-term memory-related, and orthographic long-term memory-related abilities of Iranian English as Foreign Language learners in orthography acquisition. Eighteen Iranian learners were randomly selected and non-randomly assigned into three low-labeled, mean-labeled, and high-labeled groups for the experiment. In a mixed-methods design, three Multi-Layer Perceptron Neural Network-Gray Wolf Optimizers were established to solve the mapping problem i.e., English plural noun variation orthography. ANOVA was used for analyzing Mean Squared Errors and content analysis was used for interpreting interviews and observations. Results indicated that not only the orthographic awareness and short-term memory-related abilities are more important in predicting acquisition competence than the orthographic long-term memory-related ability but also they give compensatory support to long-term memory-related ability. The proposed valid and reliable model can be used to generate hypotheses related to Individual Differences. Findings are discussed and relevant implications are drawn for teachers and practitioners.