This research provides a time series forecasting model that is hybrid which combines Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models with moving averages. For modelling stationary time series, LSTM models are utilized, while modelling non-stationary time series is done using ARIMA models. While LSTM models are more suited for capturing long-term dependencies, ARIMA models are superior in catching short-term relationships in time series data. The hybrid model combines the short-term dependency modeling of ARIMA utilising LSTM’s long-term dependency modelling. This combination leads to greater accuracy predictions for time series data that are both stationary and non-stationary. Also, Triple Exponential Moving Average (TEMA), Weighted Moving Average (WMA), Simple Moving Average (SMA), and six other moving averages were examined to determine how well the hybrid model performed. Kaufman Adaptive Moving Average (KAMA), MIDPOINT, MIDPRICE individually helps to know which methods give much precision. The study compares the hybrid model’s predicting performance to that of standalone ARIMA and LSTM models, in addition to other prominent forecasting approaches like linear regression and random forest. The findings indicate that the hybrid model surpasses the individual models and other benchmark methods, achieving increased precision in terms of Mean absolute percentage error (MAPE) and Root mean squared error (RMSE). The research also investigates the impact of different hyperparameters and model configurations on performance forecasts, giving information about the ideal settings for the hybrid model. Overall, the proposed ARIMA-LSTM hybrid model with moving averages is a promising approach for accurate and reliable stock price forecasting, which has practical implications for financial decision-making and risk management.