Death is a tragedy regardless of its form, time, or manner in which it occurs. Death can be natural, unnatural through deliberate action of other, self-inflicted, or it can be due to the environmental factors. The primary aim of this paper lies in the comprehensive analysis and synthesis of existing literature concerning unnatural mortality. The literature is surveyed from various digital repositories for analyzing the trends and patterns of unnatural deaths. The study of literature identified the common causes of deaths. This paper offers a comprehension of the existing literature by combining and summarizing the patterns, trends, and contributing elements of unnatural fatalities. Additionally, a machine learning classification model is built to categorize news articles from the dataset into five different categories based on death causes. We conducted a comparative analysis using machine learning classifiers on 20,000 news articles from the Indian Express dataset. The Decision Tree model emerged as the most effective, with an accuracy of 90%. This comparison analysis demonstrates the various strengths and shortcomings of different classifiers in categorizing death causes. Our findings shed light on mortality patterns and show how machine learning can automate the classification of death-related data, allowing for early interventions and informed policy decisions.