Groundwater quality is an important environmental concern, particularly in areas like Kanyakumari District, Tamil Nadu, where there is heavy reliance on groundwater for drinking and agriculture. To manage underground resources sustainably, understanding groundwater quality parameters is essential. In this study, a novel hybrid model that combines the strengths of the Hidden Markov Model (HMM) and Stochastic Neural Network (SNN) is employed to predict the quality of groundwater in the study area with high accuracy. Utilizing groundwater quality indicators such as pH, EC, TDS, major ions, and minor ions, along with rainfall data (since it is one of the factors influencing groundwater quality in the study area), the HMM captures latent sequential patterns within the dataset, transforming the features to enhance SNN classification. The model's performance is further optimized, achieving an overall accuracy of 97%. Additionally, the confusion matrix, classification report, Cohen Kappa score, and Matthews correlation coefficient are used to assess the model's performance. The training and testing accuracy are used to evaluate the generalization of unseen data. This study contributes to the development of advanced tools for groundwater management.