This study investigates the forecasting of life expectancy for male and female populations up to the age of 65 in Nigeria using ARIMA and NNAR models. Data spanning from 1960 to 2020 sourced from the World Bank’s Nigerian data indicators are utilized. The dataset is partitioned into training and testing sets, and ARIMA models are fitted using the auto. arima package, while NNAR models are selected using the nnectar library in R. Diagnostic checks, including Ljung-Box tests, confirm the suitability of ARIMA models, with ARIMA (2,1,0) and ARIMA (1,1,1) identified as best fits for male and female life expectancy to age 65 respectively. Comparative analysis reveals the superior forecasting accuracy of ARIMA models over NNAR models. Visual representations of actual and forecasted values further corroborate these findings. The implications of these results for healthcare planning and policy decisions in Nigeria are discussed. Future research directions include incorporating additional variables, exploring advanced modeling techniques, extending the forecasting horizon, conducting validation and sensitivity analyses, comparing models across regions, and assessing policy impacts. By addressing these avenues, we can enhance our understanding of life expectancy up to age 65 forecasting and contribute to more effective healthcare planning and policy formulation in Nigeria and beyond.