Groundwater and its upcoming crisis are the present-day concern of the scientist. This research mainly focuses on responses of groundwater dynamicity to some important drivers, viz. agricultural yield, groundwater irrigated area, groundwater draft, landuse/landcover, and stage of development. The result of this study has been done under three sections. In the first section, the spatiality of groundwater has been discussed where it has been noticed that the western side of the district groundwater level is near the surface due to low drafting and low agricultural yield. Moreover, hard rock geology in the western part disappoints the drilling process. On the eastern part, rich alluvial soil influences high agricultural yield hence groundwater level lowering down rapidly. In the second section, the nature of groundwater levels has been analyzed through the boxplot, and cluster diagram, where boxplots have been drawn over different geological facies, which depicts groundwater is highly fluctuating in hard clay geology. For example, high agricultural intensity and high groundwater draft is the characteristic feature of hard clay geology. The dendrogram in cluster analysis represents a homogeneous groundwater level fluctuating station in three different time series. Last section deals with the future of groundwater level where an artificial neural network (ANN) model has been applied to extract the predicted groundwater level for 2030. This type of environmental analysis, such as groundwater fluctuations in relation to different sensitive parameters and the use of a machine learning model, would aid potential researchers and communities in making wise groundwater use decisions.

地下水及其即将到来的危机是科学家如今关注的问题。这项研究主要集中在地下水动力对一些重要驱动力的响应上。农业产量，地下水灌溉面积，地下水消耗，土地利用/土地覆盖以及开发阶段。这项研究的结果分为以下三个部分。在第一部分中，讨论了地下水的空间性，在该区域中，由于吃水少和农业产量低，导致该地区地下水水位的西侧靠近地表。此外，西部的坚硬岩石地质使钻孔过程令人失望。在东部，丰富的冲积土影响了农业的高产，因此地下水位迅速下降。在第二部分中 通过箱线图和聚类图分析了地下水位的性质，在箱形图中绘制了不同地质相的箱形图，这表明地下水在硬粘土地质中波动很大。例如，高农业强度和高地下水位是硬粘土地质学的特征。聚类分析中的树状图代表三个不同时间序列中的均一的地下水位波动站。最后一部分介绍了地下水位的未来，其中已应用人工神经网络（ANN）模型提取了2030年的预测地下水位。此类环境分析，例如与不同敏感参数有关的地下水波动以及对地下水位的使用。机器学习模型