With growing technological advancements, the significance of prediction of natural vegetation is paramount to the research community. There are diverse technical solutions that can predict the species of natural vegetation. However, these solutions fail to provide better prediction accuracy. To handle the drawbacks encountered by the existing model, this work concentrates on modelling an integrated approach for predicting the plant species. Voluminous data is gathered to produce a massive database with diverse characteristics (parameter observation) and temporal analyses. In this work, data is collected from an online resource to give the updated status of the plant species and to estimate the factors that influence the growth of the plants. Here, the vegetation index is measured to examine the species. The varied-size data is transformed into a fixed-size module to map the plant condition. Here, Deep Neural Network (DNN) is applied over the collected/available data to provide optimal accuracy. The prediction accuracy is higher when compared to the existing image processing approaches. The proposed model is preferred by the researchers as it provides a modern environment and gives better perspectives to the research community with the growing plant condition. The simulation is done in MATLAB 2020a. The proposed model offers better trade-offs compared to existing approaches. Various metrics like accuracy, F-measure, recall, precision, and error rate are evaluated to understand the vegetation in a better way.