This study aims to identify a suitable Machine Learning (ML) and Computer Vision (CV) approach for monitoring and recording the variables that influence crop output. Agricultural production relies heavily on weather conditions, which are intricately linked to each other. Climate change plays a crucial role in causing biotic and abiotic stresses on plants, resulting in a detrimental impact on global agriculture. This paper aims to assess the influence of climate change, specifically the impacts of rainfall and temperature, on production. In this study, a CNN-LSTM machine learning model is utilized to investigate the impact of climate change on production. Climate change is a worldwide phenomenon that presents significant challenges across various sectors, including agriculture, manufacturing, and energy production. The aim of this research is to evaluate the effects of climate change on production efficiency, resource allocation, and overall performance.