The growing use of remote and mass-recruitment tactics has led to the realization of a demand in the intelligent and impartial interview systems that can screen the candidates out of the conventional resume screening. In this paper, the Author describes an AI-based Virtual Interviewer System which combines with natural language processing, multimodal emotion recognition, and adaptive question generation to facilitate automated evaluation of candidates. The system allows the applicants to post their resumes, schedule interviews and engage in virtual interview in real time using a web-based interface created with Flask. Semantic similarity algorithms between resumes and job descriptions are used to conduct resume parsing and candidate-role matching based on transformer-based NLP models, which are accessed via the Gemini API. In interviews, speech of candidates is transcribed and analyzed according to their relevance, fluency and accuracy in the domain, whereas facial expressions are recognized with the DeepFace to detect emotional cues, including confidence and engagement. Depending on real time performance, the system is dynamic in adapting interview questions to probe technical and behavioral stability. Weighted fusion model is a combination of linguistic, emotional, and contextual measures that create structural scorecards to recruiters. Human ratings, low latency and scalable performance under concurrent interview loads are all shown to be well correlated with experimental evaluation. The suggested system allows increasing efficiency, fairness, and transparency in the recruitment process, which presents a feasible answer to the contemporary hiring setting.