We investigate trends in crime in Trinidad and Tobago using a data-driven approachto understand temporal trends and socioeconomic drivers of major offenses such asmurder, robbery, and larceny. The analysis integrated official police crime records,national census data, and employed time-series visualization, regression modeling,and normalization techniques to uncover patterns and insights related to fluctuationsin crime rates and their correlation with social vulnerability factors. Results indicatedthat temporal trends, population-normalized rates, and predictive policing forecastscontributed to a clearer understanding of how socioeconomics and public safety shapednational crime dynamics. This research demonstrated how data science methods cansupport evidence-based reasoning in the study of criminology.