India is the fifth-largest economy and stands second in the world regarding agricultural output. With the growing population, it is a need of the hour to implement smarter agricultural practices with the benefit of cutting-edge technologies. This ensures food security, efficient management of agriculture, and smart decision-making processes. Utilizing pioneering data science methodologies can help fight food scarcity and effectively manage crop diseases while empowering the agrarian community. The present preliminary work emphasizes the implementation of a time-series analysis of agricultural production studied in the state of Telangana, India. Chronological trend-based algorithms were applied to the agricultural production data covering the six most important crops in the state—castor, cotton, groundnut, jowar, rice, and sesamum. Among various methods of monitoring, predicting, and assessing crop production, time-series models, such as ARIMA, can be of key importance due to the availability of required information. The capabilities of the aforementioned algorithms in understanding the patterns and anomalies enable a better understanding of the results and faster decision-making. Reinforcing and working on the mentioned techniques, it is now conceivable to conduct timely, comprehensive scrutiny, real-time assessment, monitoring, and control of how events unfold in agriculture. Similarly, acquiring and using data from a more localized area makes it possible to predict production patterns in greater detail. This can help in better managing the agricultural situation with greater detail.