Over the past few decades, the utilization of solar power has gained immense significance in the power grid, gradually taking over the responsibilities of fossil fuel-based power. Therefore, accurate short-term forecasting of photovoltaic power output is crucial for making informed decisions regarding power generation, transmission, and distribution. Consequently, many machine-learning models were used to reliably forecast solar power. In this study, four machine learning models have been studied which are Artificial Neural Networks, Convolutional Neural Networks, Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM). They have been used to forecast the solar power of Nhi Ha solar farm in short-term. First, data from Nhi Ha solar farm were collected and underwent preprocessing before being utilized by aforementioned distinct machine learning models. The Root Mean Squared Error (RMSE) and normalized RMSE (N-RMSE) obtained from the models will be analyzed to determine the most effective model for short-term solar power forecasting. Following a comprehensive analysis, it has been determined that all four models have produced favorable outcomes, with low values of RMSE and N-RMSE indicating high levels of reliability and accuracy. Of the models considered, the LSTM and ELM models have demonstrated better performance, making them the good choice for precise short-term solar power forecasting.