The interpretation of the geographic diversity pattern can be explored by establishing an effective species distribution model (SDM). This work aims to identify the relationship between the Indian ecosystem and map the predictions of the region-specific Magnoliopsida and Aves classes under the kingdom of Plantae-Animalia species occurrences in India. In this effort, the GBIF-based dataset is utilized over the Indian boundary while integrating the satellite image with the time series data of species occurrences. Scientific approaches developed the multimodal ensemble (MME) integrated neural network model by combining Resnet-6, XGB regression, and region-specific top-20 multilabel species distribution methods. Furthermore, this model is exploited with the patches of Satellite images, Climatic, and Landsat cubes through a deep convolutional neural network and Resnet-6. Different preprocessing approaches like Albumentations, dynamic transformation, and PCA-based reduction are also utilized for multiclass-based species prediction. Here, different combinations of predictor data sets with optimized hyperparameter tuning provide satisfactory accuracy in the predictions of species occurrences. In particular, here we have evaluated the predictions that impact the association of Aves species with Magnoliopsida plant species. This paper presents the ROC curve in the resultant analysis of the multimodal ensemble-based neural network model.