This research dives into temperature prediction, focusing on its practical applications and how advanced Machine Learning techniques can improve accuracy. It specifically examines the use of long short-term memory (LSTM) networks in enhancing forecasting capabilities. The study uses data from Delhi, India, to train and assess the LSTM model. This paper finds key gaps in temperature forecasting research and explores their impact on industries and daily life. Accurate temperature prediction are important for sectors such as agriculture, transportation, and disaster management, where even small improvements can have significant effects. The research provides a detailed examination of the LSTM model, including its structure, validation strategy, and evaluation process. LSTM networks are ideal for temperature forecasting because they can effectively capture long-term patterns in time series data. The study demonstrates the effectiveness of LSTM networks in improving temperature prediction accuracy and discusses their potential to transform industries relying on accurate forecasts. This research trained a model with an efficient evaluation of MSE 0.034.