The number of publicly known Common Vulnerabilities and Exposures (CVEs) submitted to the national vulnerability database (NVD) has increased significantly due to the increasing complexity of modern computing systems. However, it is cumbersome to extract useful information from this large corpus of unstructured data and find meaningful trends over time without proper tools. As a result, vulnerability management has become an important task in cybersecurity [1]. Recently, there has been a lot of analytical studies on the openly available Common Weaknesses Enumeration (CWE) database. These studies mostly focus on connecting the CVE entries in the NVD to a corresponding weakness in the CWE dataset using traditional Machine Learning (ML) approaches. However, almost all existing works in the literature focus on software vulnerabilities, and hardware vulnerabilities are often far from the focus of the analysis. Unlike software vulnerabilities, hardware vulnerabilities are not easily patchable and cannot be fully mitigated. It was not until February 2020 that the MITRE added a new category focused only on hardware exposures. Moreover, the CVE instances are manually connected to CWE entries by cyber security experts, however there are some inconsistencies due to human error. Furthermore, there is a need for a novel ML-assisted framework that extracts useful information from these openly available datasets, find trends, analyze the relationships and similarities among various data points, and provide insight into how to prevent emerging vulnerabilities and impacts. Thus, we aim to propose an ML-based framework for vulnerability and impact vector classification focusing on the hardware vulnerabilities in the IoT domain. Our proposed approach will be equipped with an ontology-driven framework, which updates the ontology in an automated fashion and aims to identify similar patterns of vulnerabilities over time using Generative Pre-trained Transformers (GPT) and Large Language Models (LLMs). By exposing pre-trained models such as GPT-3 API to CVE datasets, and fine tuning it using hugging face framework we will develop a smart framework which helps mitigate the impacts of vulnerabilities or predict and prevent future vulnerabilities. Our proposed ML-based approach in conjunction with our proposed ontology-driven framework will provide recommendations in regards to mitigation strategies to reduce the impacts vulnerabilities.