The escalating impact of climate change on food agriculture necessitates advanced analytical approaches for accurate forecasting and mitigation strategies. This study employs Stacked Long Short-Term Memory (LSTM) networks, a sophisticated variant of Recurrent Neural Networks (RNNs), to analyze and predict the influence of climate change on agricultural outputs. Stacked LSTMs, known for their efficacy in processing sequential and time-series data, are utilized to decipher the complex interdependencies between various climatic factors and agricultural productivity