In the current competitive era, every aspect demands computational efficiency. This struggle to be computationally efficient motivates humans to acquire more and more skills to be on the top of this efficiency queue. Right from birth individuals are trained to acquire more and more skills; not only education but the current era demands overall physical, emotional as well as mental stability and potency. This scenario has increased the value of sports in addition to being a means of recreation, it has become the most preferred career among the modern generation. While a few sports are very famous among which cricket happens to be one. In the process of Talent Identification in Cricket, the talent is quantified in terms of 28 parameters. If a performer possesses the required range for a set of parameters, the performer is identified as talented otherwise not talented. Now an individual who has a desire to be a cricketer but because of lagging in a few parameters the performer is not been identified as talented. In this study, we propose an expert system for talent enhancement of these non-talented performers that will accept as feed the measured values of the parameter set and then output the explanatory guidelines for talent enhancement of the areas where the performer is found to lag. The design and implementation of the system have been done in the Django Framework. The base of the expert system is Forward chaining. The system has been validated on real data of early-age performers that were primarily collected from the schools. The Study has been generalized for both male and female genders. Without the inclusion of any human-centric biases, the system would analyze the parameters and suggest the enhancement guidelines to the performers in a timely efficient manner.