Nitrous oxide (N2O) in riverbeds affects hydrological processes by contributing to the greenhouse effect, indicating poor water quality, disrupting biogeochemical cycling, and linking to eutrophication. Elevated N2O levels signal environmental issues, impacting aquatic life and necessitating precise forecasting for effective environmental management and reduced greenhouse gas emissions. Precisely forecasting nitrous oxide (N<inf>2</inf>O) emissions from riverbeds is paramount for effective environmental management, given its significant potency as a greenhouse gas. This study focuses on the difficulties related to spatial feature extraction and modeling accuracy in predicting N<inf>2</inf>O in riverbeds in Tamil Nadu. To address the obstacles, the research suggests utilizing the Deep Learning Based Prediction of Nitrous Oxide Contaminants (DL-PNOC), which studies the N<inf>2</inf>O contaminants in water using Region-based Convolutional Neural Network (RCNN) for spatial feature extraction, to predict nitrous oxide contaminants. The study is centered on the Cauvery River Basin located in Tamil Nadu, where the emission of N<inf>2</inf>O is a matter of environment. The outcomes encompass the specialized N<inf>2</inf>O contaminant model for riverbeds and the implementation of RCNN achieves precise N<inf>2</inf>O forecasting. The DL-PNOC approach combines a contaminant model with RCNN deep learning techniques to capture spatial characteristics and predict N<inf>2</inf>O pollutants accurately. Furthermore, using the River Bed Dynamics Simulator reinforces the dependability of the findings. The DL-PNOC approach has exhibited encouraging results, as evidenced by the following metrics: a high IoU of 88.66%, precision of 88.96%, recall of 90.03%, F1 score of 89.22%, and low RMSE and MAE values of 9.14% and 7.59%, respectively. The findings highlight the efficacy of the DL-PNOC approach in precisely forecasting N<inf>2</inf>O pollutants in river sediments. © 2024 Elsevier B.V., All rights reserved.