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Improving life expectancy, which is falling and declining globally, is getting harder, especially given the limited economic resources in most parts of the world. This study seeks to identify the most significant factors that positively and negatively affect life expectancy, separating them from less significant factors. This way, specific areas can be identified to channel scarce economic resources towards increasing life expectancy. Using data science techniques on a dataset comprising economic, immunological, health, personal, and social attributes, we have been able to achieve this. Furthermore, four machine learning tree regression algorithms were employed using the identified attributes to develop and evaluate life expectancy prediction models. The extremely randomized tree model performed best using evaluation matrices: MAE, RMSE, R2, and CV score. This research can help governments, especially in low-income, developing countries, make better decisions and investments, as well as help digital health experts develop technologies that could improve life expectancy.