This work employs both supervised and unsupervised machine learning techniques to examine firearm-related fatalities in the US and identify trends, patterns, and risk factors within the data. During the supervised learning phase, techniques such as logistic regression, decision trees, random forests, and neural networks were used to predict the kind of death (suicide, homicide, accidental, or unknown) based on demographic data like sex, age, race, place, and education. Findings show that the neural network and random forest models exhibit promising precision and recall values across several classes, and that they obtained the highest accuracy, reaching 79.88% and 83.59%, respectively. Using clustering techniques including Agglomerative clustering, K-means, and Gaussian mixture models, gun-related fatalities were categorized based on demographic and temporal data during the unsupervised learning stage. The analysis revealed distinct clusters of deaths, providing insights into the varying patterns and trends over time and across demographic groups. The K-means algorithm, with a silhouette score of 0.42, demonstrated meaningful separation among clusters. The research contributes to understanding the complex dynamics of gun-related deaths, shedding light on both individual risk factors and broader trends. However, further analysis could explore additional dimensions of the dataset or delve deeper into the interpretation of clustering results. The study also highlights how crucial it is to take into consideration the moral consequences and constraints of machine learning applications in complex fields like public health.