Water is a fundamental and crucial natural resource for human survival. However, the global demand for water is increasing, leading to a subsequent decrease in water availability. This study addresses the critical need for improved water resource forecasting models amidst global water scarcity concerns exacerbated by climate change. This study uses the best weather and water resource forecasting model for sustainable development. Employing a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) approach, the research enhances drought prediction capabilities by integrating secondary data of the rainfall, temperature, and ground and surface water supplies. The primary objective is to forecast water resources under changing climatic conditions, facilitating the development of early warning systems for vulnerable regions. The results from the LSTM model show an increased trend in temperature and rainfall patterns. However, a relatively unstable decrease in rainfall is observed. The best statistical analysis result was observed with the LSTM model; the model’s accuracy was 99%, showing that it was quite good at presenting the obtained precipitation, temperature, and water data. Meanwhile, the value of the root mean squared error (RMSE) was about 13, 15, and 20, respectively. Therefore, the study’s results highlight that the LSTM model was the most suitable among the artificial neural networks for forecasting the weather, rainfall, and water resources. This study will help weather forecasting, agriculture, and meteorological departments be effective for water resource forecasting.

Ensuring food security in Pakistan, particularly for rice production, is a critical challenge due to increasing population demands and the growing impact of climate change variability. Accurate estimation of rice crop yields is essential for optimizing resource allocation, managing supply chains, and forecasting economic growth while minimizing agricultural losses. This study utilizes a Deep Neural Network (DNN) to predict rice yields in Pakistan by analyzing the effects of maximum temperature and precipitation trends under high-emission scenarios (SSP5-8.5) derived from CMIP6 climate models. Historical (1980–2014) and future (2015–2100) climate projections were evaluated using key variables, including precipitation, meteorological conditions, cultivated area, and crop yields. Results from CMIP6 SSP5-8.5 indicate a significant rise in maximum temperatures and increased precipitation variability, exacerbating risks to rice crop yields. DNN demonstrated superior accuracy in forecasting these trends, achieving high R-squared values and low error metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings reveal that Pakistan, particularly Eastern South Asia, is highly vulnerable to climate extremes, with severe implications for rice production and agricultural sustainability. These results highlight the urgent need for policymakers to adopt climate adaptation strategies, including advanced predictive modeling and resilient agricultural practices, to safeguard rice production and ensure long-term food security in Pakistan’s monsoon-dependent regions. This study aligns with Sustainable Development Goal 2 (Zero Hunger) by contributing to food security and sustainable agricultural development, and with Sustainable Development Goal 13 (Climate Action) by addressing climate change impacts on agriculture and promoting resilience in rice production systems.

Food and water security are critical challenges in Pakistan, exacerbated by rapid population growth, climate variability, and limited resources. This study explores the application of machine learning techniques to address these issues. We specifically examine the dimensions of food and water security in Pakistan, employing data-driven methods to enhance crop yield predictions, food production forecasting, and water resource management. Using secondary data, we refine machine learning models, such as random forest and linear regression, to analyze water availability, crop yield, and crop production. These models aim to optimize resource distribution, improve irrigation efficiency, and minimize water waste. We propose developing AI-based predictions to address food and water crises proactively. Our findings indicate that food insecurity persists in Pakistan, worsened by uneven distribution. Given the country’s high dependence on irrigation for crop production, we analyze the impact of population growth on food production and water demand. We recommend a comprehensive strategy that includes infrastructure development, improved water use efficiency in agriculture, and policy adjustments to balance food imports and exports.

This study examines the impact of drought on the agricultural productivity in Pakistan, focusing on wheat yield prediction. Using the Long Short-term Memor