A processor implemented method for performance and health monitoring to optimize operation of a pulverizer mill, the method comprising: receiving, via one or more hardware processors, a plurality of data as input data from one or more data sources; preprocessing, via the one or more hardware processors, the plurality of data and storing the preprocessed plurality of data in a database; soft sensing, via the one or more hardware processors, a plurality of key performance and health parameters of the pulverizer mill and coal quality parameters using the preprocessed plurality of data and a set of soft sensing models, wherein the set of soft sensing models comprises pre-generated models; predicting, via the one or more hardware processors, a plurality of key operating events of the pulverizer mill using the soft sensed plurality of key performance, health parameters, coal quality parameters and the preprocessed plurality of data, wherein the plurality of key operating events are associated with adverse conditions in the pulverizer mill; estimating, via the one or more hardware processors, a current health index and a future health index using a plurality of health index models, wherein the health index is indicative of the health of the pulverizer mill, wherein the plurality of health index models are pre-generated models; comparing, via the one or more hardware processors, the current health index and the future health index with a threshold health index, diagnose the current health and future health of the pulverizer mill, wherein the diagnosis comprises of identifying and localization of faults, a root cause analysis of the faults, and estimating a remaining useful life of the pulverizer mill and its components; providing, via the one or more hardware processors, a set of operating recommendations to the pulverizer mill to mitigate the adverse conditions in the pulverizer mill based on the plurality of key operating events and the diagnosis; and providing, via the one or more hardware processors, optimal maintenance and scheduling recommendations using maintenance optimizer models, based on the estimated the current health index, the future health index and the estimated remaining useful life of pulverizer mill. receiving, via one or more hardware processors, a plurality of data as input data from one or more data sources; preprocessing, via the one or more hardware processors, the plurality of data and storing the preprocessed plurality of data in a database; soft sensing, via the one or more hardware processors, a plurality of key performance and health parameters of the pulverizer mill and coal quality parameters using the preprocessed plurality of data and a set of soft sensing models, wherein the set of soft sensing models comprises pre-generated models; predicting, via the one or more hardware processors, a plurality of key operating events of the pulverizer mill using the soft sensed plurality of key performance, health parameters, coal quality parameters and the preprocessed plurality of data, wherein the plurality of key operating events are associated with adverse conditions in the pulverizer mill; estimating, via the one or more hardware processors, a current health index and a future health index using a plurality of health index models, wherein the health index is indicative of the health of the pulverizer mill, wherein the plurality of health index models are pre-generated models; comparing, via the one or more hardware processors, the current health index and the future health index with a threshold health index, diagnose the current health and future health of the pulverizer mill, wherein the diagnosis comprises of identifying and localization of faults, a root cause analysis of the faults, and estimating a remaining useful life of the pulverizer mill and its components; providing, via the one or more hardware processors, a set of operating recommendations to the pulverizer mill to mitigate the adverse conditions in the pulverizer mill based on the plurality of key operating events and the diagnosis; and providing, via the one or more hardware processors, optimal maintenance and scheduling recommendations using maintenance optimizer models, based on the estimated the current health index, the future health index and the estimated remaining useful life of pulverizer mill.