Wire electrical discharge machining (WEDM) is a complex machining process that requires precise control of various parameters to ensure optimal performance and product quality. This paper presents a data-driven approach using digital twin modeling to predict the wire electrode temperature in WEDM, by leveraging real-time data from temperature sensors integrated into the WEDM machine with a specialized design fixture. The DT model accurately replicates the physical process and provides insights into the wire electrode temperature distribution. The present DT model builds up in Python with experimental data and validate with 12 set of experimental data. The validation result shows the noise reduction in the developed DT model. Real-time monitoring with the digital twin model allows for predictive analysis of temperature fluctuations that may lead to wire rupture. The digital twin can identify potential rupture risks and suggest adjustments to prevent overheating and wire breakage.