Educational Institutions are facing enormous tasks of marking and grading students at the end of every examination within the shortest possible time. Marking theoretical essay questions which involves thousands of examinees can be biased, subjective and time-consuming, leading to variation in grades awarded by different human assessors. This study presents an Essay Grading System called Intelligent Natural Language Processing Essay Grading System (iNLPEGS) with high accuracy percentage and minimal loss function for scoring assessment that can accommodate more robust questions. Secondary dataset collected from Kaggle provided by The Hewlett Foundation was used to aid semantic analysis and Part of Speech tagging. Assemblage of Computer Science questions and answers were collected from Babcock University Computer Science Department to create a more robust dataset to ensure high reliability. An Intelligent Natural Language Processing Essay Grading Model was designed based on Enhanced Latent Semantic Analysis using Part of Speech n-gram Inverse Document Frequency. Web based application was developed using Django, Gensim, Jupyter Notebook and Anaconda as the development tools due to availability of several Python libraries with SQLite as the database. Results of performance evaluation of iNLPEGS showed accuracy of 89.03% and error of 10.97% connoting that there is very little difference between scores from the developed intelligent essay grading system and a human grader. Also, the loss function from Root Mean Square Error (RSME) showed value of 0.620 which is very small and thus signifies closeness to the line of best fit from the regression equation.