In recent years, speech recognition technology has gained significant importance in web-based applications due to its ability to enable hands-free interaction, enhance user convenience, and improve accessibility for individuals with physical or hearing impairments. The rapid advancement of artificial intelligence and natural language processing techniques has made voice-driven systems more accurate, responsive, and adaptable across multiple domains such as education, healthcare, customer service, and smart environments. Voice-to-text conversion, in particular, has become a widely adopted feature in modern applications, allowing spoken language to be transformed into written text efficiently and in real time. This article presents the design and implementation of a Voice-to-Text Based Threat Detection and Alert System developed using Python and the Django web framework. The system is designed as a web-based application that captures spoken input directly through a browser interface, processes the audio using the SpeechRecognition library, and converts it into readable textual format. The transcribed text is displayed instantly on the application interface, ensuring smooth user interaction and minimal processing delay. By leveraging Django’s structured backend architecture, the system ensures secure data handling, efficient request processing, and scalable deployment capabilities. Unlike conventional voice-to-text systems that focus solely on transcription accuracy and speed, this system extends functionality by incorporating an intelligent threat detection module. After the speech is converted into text, the system analyzes the content using predefined keywords, logical filtering rules, and pattern recognition techniques to identify potentially harmful, abusive, or threatening language. This additional layer of analysis transforms the system from a simple transcription tool into a proactive safety monitoring solution. When suspicious or dangerous content is detected, the system automatically triggers an alert mechanism. A notification is generated and displayed on a dedicated monitoring dashboard designed for parents, guardians, or administrators. The alert includes relevant details such as the identified keyword and transcript segment, enabling timely awareness and intervention. All conversation logs and alert records are securely stored in the database for documentation and future review. By combining real-time speech processing, web technologies, structured data management, and safety monitoring mechanisms, the proposed system offers an efficient, scalable, and user-friendly solution for secure communication environments. The integration of voice recognition with automated threat detection demonstrates how modern technologies can be utilized not only to enhance accessibility and convenience but also to strengthen digital safety and proactive supervision.