To address the rising energy demands in industrial and public sectors, integrating zero‐carbon emission energy sources into the power grid is crucial. Smart grids, equipped with advanced sensing, computing, and communication technologies, offer an efficient way to incorporate renewable energy resources and manage power systems effectively. However, improving solar energy efficiency, which currently contributes around 3.6% to global electricity, is a challenge in smart grid infrastructures. This research tackles this issue by deploying machine learning models, specifically recurrent neural network (RNN), long short‐term memory (LSTM), and gate recurrent unit (GRU), to predict measurements that could enhance solar power generation in smart grids. The objective is to boost both performance and accuracy of solar power generation in the smart grid. The study conducts experimental analyses and performance evaluations of these models in smart grid environments, considering factors like power output, irradiance, and performance ratio. The results, presented through graphical visualizations, show notable improvements, particularly with the LSTM model, which achieves a 97% accuracy, outperforming the RNN and GRU models. This outcome highlights the LSTM model's effectiveness in accurately predicting measurements, thereby advancing solar power generation efficiency in the smart grid framework.