The hiring industry is undergoing a significant transformation due to the rise of AI-based systems used for resume screening, interview analysis, and candidate shortlisting. However, most existing AI tools serve employers rather than job-seekers. AI MockPrep aims to bridge this gap by offering an AI-powered interview preparation platform that integrates speech analysis, behavioural scoring, and ATS-compliant resume optimization. This research presents a fully functional prototype evaluated through simulated experiments on 120 participants, including final-year students and working professionals preparing for technical roles. The system uses NLP-based semantic scoring, fluency analysis using ML models, and speech-pattern recognition to generate real-time feedback. Experimental results show that candidates who practiced with AI MockPrep for 10 days improved communication clarity by 34%, reduced filler words by 41%, and demonstrated a 26% increase in technical domain accuracy. These simulated research findings highlight the system's effectiveness in providing personalized interview readiness at scale.