Tool wear monitoring is critical for quality and precision of manufacturing of parts in the machining industry. Existing tool wear monitoring and prediction methods are sensor based, costly and pose challenge in ease of implementation. Embodiments herein provide method and system for monitoring tool wear to estimate Remaining Useful Life (RUL) of a tool in machining is disclosed. The method provides a tool wear model, which combines tool wear physics with data fitting, capture practical considerations of a machining system, which makes the tool wear prediction and estimated RUL more stable, reliable and robust. Further, provides cost effective and practical solution. The disclosed physics based tool wear model for RUL estimation captures privilege of physics of tool wear and easily accessible data from CNC machine to monitor and predict tool wear and RUL of the tool in real-time.