An artificial intelligent fuel cell system, comprising: a fuel cell stack in which a plurality of unit cells for generating electric energy with an electrochemical reaction is combined; a sensor unit which measures in real time data about temperature, pressure, humidity, and flow rates of reaction gases, and cooling water, and current and voltage data during an operation of a fuel cell; an artificial intelligent unit which collects the data measured by the sensor unit with a predetermined time interval, generates a model for predicting and controlling performance of the fuel cell through the learning and analysis of the collected data, diagnoses a state of the fuel cell stack by comparing a prediction value from the generated model with data measured in real time, and generates a control signal for changing an operation condition of the fuel cell stack; and a control unit which changes the operation condition of the fuel cell stack according to the generated control signal, wherein the artificial intelligent unit includes: a data collecting unit which collects the data about the temperature, the pressure, the humidity, the flow rates, the current, and the voltage measured by the sensor unit in real time with a predetermined time interval; a data learning and model generating unit which learns and analyzes the data collected with the predetermined time interval by using machine learning and a time-series analysis and generates a model for predicting and controlling performance of the fuel cell; and a performance predicting and diagnosing unit which compares the prediction value from the generated model with measured data, distinguishes a change in performance over time of the fuel cell stack into a first state and a second state through a time-series trend analysis of the data collected by the data collecting unit and the prediction value, and diagnoses the performance change state, and generates the control signal for changing an operation condition of the fuel cell stack according to the diagnosed state of the fuel cell stack and makes the control unit change the operation condition of the fuel cell stack, and the first state is a short-term performance degradation state, and the second state is a long-term performance degradation state. a fuel cell stack in which a plurality of unit cells for generating electric energy with an electrochemical reaction is combined; a sensor unit which measures in real time data about temperature, pressure, humidity, and flow rates of reaction gases, and cooling water, and current and voltage data during an operation of a fuel cell; an artificial intelligent unit which collects the data measured by the sensor unit with a predetermined time interval, generates a model for predicting and controlling performance of the fuel cell through the learning and analysis of the collected data, diagnoses a state of the fuel cell stack by comparing a prediction value from the generated model with data measured in real time, and generates a control signal for changing an operation condition of the fuel cell stack; and a control unit which changes the operation condition of the fuel cell stack according to the generated control signal, wherein the artificial intelligent unit includes: a data collecting unit which collects the data about the temperature, the pressure, the humidity, the flow rates, the current, and the voltage measured by the sensor unit in real time with a predetermined time interval; a data learning and model generating unit which learns and analyzes the data collected with the predetermined time interval by using machine learning and a time-series analysis and generates a model for predicting and controlling performance of the fuel cell; and a performance predicting and diagnosing unit which compares the prediction value from the generated model with measured data, distinguishes a change in performance over time of the fuel cell stack into a first state and a second state through a time-series trend analysis of the data collected by the data collecting unit and the prediction value, and diagnoses the performance change state, and generates the control signal for changing an operation condition of the fuel cell stack according to the diagnosed state of the fuel cell stack and makes the control unit change the operation condition of the fuel cell stack, and a data collecting unit which collects the data about the temperature, the pressure, the humidity, the flow rates, the current, and the voltage measured by the sensor unit in real time with a predetermined time interval; a data learning and model generating unit which learns and analyzes the data collected with the predetermined time interval by using machine learning and a time-series analysis and generates a model for predicting and controlling performance of the fuel cell; and a performance predicting and diagnosing unit which compares the prediction value from the generated model with measured data, distinguishes a change in performance over time of the fuel cell stack into a first state and a second state through a time-series trend analysis of the data collected by the data collecting unit and the prediction value, and diagnoses the performance change state, and generates the control signal for changing an operation condition of the fuel cell stack according to the diagnosed state of the fuel cell stack and makes the control unit change the operation condition of the fuel cell stack, and the first state is a short-term performance degradation state, and the second state is a long-term performance degradation state.