A processor implemented method for optimization of an ironmaking plant, comprising: collecting data from a plurality of data sources of the ironmaking plant as input data, via one or more hardware processors; pre-processing the input data to generate a pre-processed data, via the one or more hardware processors, comprising conditioning the input data to satisfy one or more pre-defined data quality requirements; obtaining, via the one or more hardware processors, a simulated data using the pre-processed data and a plurality of soft sensors, wherein the simulated data is integrated with the pre-processed data to obtain an integrated data; determining, via the one or more hardware processors, a first set of parameters using the integrated data and a plurality of data-driven models, for a plurality of pre-ironmaking units of the ironmaking plant; obtaining, via the one or more hardware processors, a second set of parameters by combining the integrated data with the first set of parameters; forecasting, via the one or more hardware processors, a set of key performance indicators (KPIs) using the second set of parameters and the plurality of data-driven models of the ironmaking plant; determining a stability score of a blast furnace of the ironmaking plant, using the forecasted set of KPIs, the integrated data, and a plurality of stability score soft-sensors, via the one or more hardware processors, wherein the stability score represents a determined operating stability of the blast furnace; monitoring for deviation of one or more of the forecasted KPIs or the stability score, from a corresponding pre-determined threshold, via the one or more hardware processors; configuring optimization of one or more of the forecasted KPIs, using the determined stability score as a constraint, if the deviation from the pre-determined threshold is detected, via the one or more hardware processors; and generating one or more recommendations based on the configured optimization of the one or more KPIs, via the one or more hardware processors. collecting data from a plurality of data sources of the ironmaking plant as input data, via one or more hardware processors; pre-processing the input data to generate a pre-processed data, via the one or more hardware processors, comprising conditioning the input data to satisfy one or more pre-defined data quality requirements; obtaining, via the one or more hardware processors, a simulated data using the pre-processed data and a plurality of soft sensors, wherein the simulated data is integrated with the pre-processed data to obtain an integrated data; determining, via the one or more hardware processors, a first set of parameters using the integrated data and a plurality of data-driven models, for a plurality of pre-ironmaking units of the ironmaking plant; obtaining, via the one or more hardware processors, a second set of parameters by combining the integrated data with the first set of parameters; forecasting, via the one or more hardware processors, a set of key performance indicators (KPIs) using the second set of parameters and the plurality of data-driven models of the ironmaking plant; determining a stability score of a blast furnace of the ironmaking plant, using the forecasted set of KPIs, the integrated data, and a plurality of stability score soft-sensors, via the one or more hardware processors, wherein the stability score represents a determined operating stability of the blast furnace; monitoring for deviation of one or more of the forecasted KPIs or the stability score, from a corresponding pre-determined threshold, via the one or more hardware processors; configuring optimization of one or more of the forecasted KPIs, using the determined stability score as a constraint, if the deviation from the pre-determined threshold is detected, via the one or more hardware processors; and generating one or more recommendations based on the configured optimization of the one or more KPIs, via the one or more hardware processors.