Evaluating mining works tenders is a critical yet labor-intensive process that requires assessing bids’ technical, financial, and regulatory compliance aspects. Traditional methods often involve manual scrutiny, which is time-consuming, prone to human bias, and inefficient when dealing with large volumes of tenders. This paper proposes an AI-driven automated system for tender evaluation, leveraging Natural Language Processing (NLP) and Machine Learning (ML) to enhance accuracy, fairness, and efficiency. The system is designed to extract and analyze relevant information from tender documents, handling diverse formats using NLP-based text parsing. Machine learning algorithms evaluate bids against predefined criteria, ensuring objective assessment while reducing manual intervention. The system also facilitates processing many tenders simultaneously, allowing organizations to optimize procurement workflows. Furthermore, the proposed solution is designed to seamlessly integrate with existing enterprise systems, ensuring ease of use for employees and stakeholders. By automating the tender evaluation process, the system minimizes human errors, accelerates decision-making, and enhances transparency in bid selection.