Advances in Artificial Intelligence (AI) have made it the new frontier for recruitment practices by bringing in automation, data-enabled Decision Making Systems (DMP), and predictive analytics into the Recruitment Process (RP). Most traditional PR tends to be lengthy, biased, or inefficient. With an increasing demand for efficient and unbiased recruitment, AI-driven Talent Acquisition Service (TAS) solutions have found their place. This research explains the AI-powered recruitment framework called AI-assisted Recruitment Framework (AI-RF), which includes automated resume screening, chatbot-driven applicant appointment, predictive analytics, AI-powered interview analysis, and candidate bias mitigation. The research implements the framework using an internally developed publicly available dataset. To automate and enhance various stages of the RP, the research work uses Machine Learning (ML) and Deep Learning (DL) models as Modified Bidirectional Encoder Representations from Transformers (BERT), Convolutional Neural Network (CNN), and Random Forest (RF). The proposed model AI-RF is evaluated using the performance metrics precision, recall, F1-score, and bias reduction percentage. The results suggest that the AI-driven RP improves screening accuracy by 92%, response time reduced by 70%, improves job matching accuracy by 30%, and mitigates hiring biases by 85%. The comparison shows that DL outperforms other models in terms of efficiency and fairness in hiring. The AI-powered RP can enhance human bias in DMS and improve talent acquisition efficiency