The proposed method involves the fault analysis of the inverter switches present in the multi-level inverter (MLI) circuitry. The decision tree machine learning algorithm is incorporated for the fault analysis of the inverter switches. The multi-level inverter utilized in this work is a 7-level switched ladder multi-level inverter. There is 4 number of switches in the design of a 7-level inverter driven by the non-carrier digital pulse width modulation signals. The non-carried-based digital pulse-width modulator (DPWM) generation is generated using the event angle for the 7-level of the switched ladder inverter. The proposed method investigates the stuck-at-fault occurrences of the 4 switches in the inverter by manipulating the decision tree parameters such as entropy, information gain, and decision tree. Based on the decision tree, the very high-speed integrated circuit hardware description language (VHDL) code is developed by making use of the behavioral modeling and validated for the power, area in the Xilinx Vivado tool. The real-time feasibility is verified for the proposed method by synthesizing the developed VHDL code in the field programmable gate array (FPGA) device.