This paper proposes an AI enabled taxonomy model to measure and estimate the replaceability of occupations in the era of automation. With a dataset containing more than 94,000 of jobs postings and job descriptions, the job roles have been classified into three categories such as High, Medium, and Low regarding the possibility of being replaced by AIs. The method of supervised learning was used, where rule-based labeling and TF-IDF textual data (specifically job titles, job requirements, and skills) vectorization were used. Random Forest classifier showed a perfect result, precision, recall and F1-score of 100% on every label. Graphical representations in form of the bar charts described major findings: IT-based jobs like the Network Administrators and Data Entry Clerks are the jobs that automated by Artificial Intelligences (AIs) are most vulnerable to automation and that jobs in the areas like Research Analysts and Psychologists are very robust. A confusion matrix assured that the accuracy of the model was flawless, which indeed testifies to the correctness of the feature selection and learning policy. This publication is directly concerned with the academic and labor market restructuring, and provides evidence as to how the educational establishments can create more relevant curricula and become prepared to meet the needs of occupational market in the future. The study runs parallel to the theme; AI-Driven Classification of Job Replaceability: Predicting Vulnerable and Resilient Roles to Reorganize Academic Labor and Human Capital in the University. The insights will help the stakeholders proactively prepare the students and professionals towards a rapidly changing digital economy.