The efficiency of an assembly line is a critical factor in determining overall production performance in modern manufacturing industries. This study presents a detailed data analysis of an assembly line with the objective of evaluating production flow, identifying bottlenecks, and measuring key performance indicators such as cycle time, throughput, and defect rates. Data collected from different stages of the assembly process were systematically analyzed to assess trends, deviations, and their impact on overall output. The results indicate that certain stages contributed significantly to delays due to imbalance in workload distribution and machine downtime. Statistical tools and performance metrics were employed to suggest improvements for line balancing, quality enhancement, and resource utilization. The study concludes that applying data-driven approaches not only improves efficiency and reduces wastage but also enhances decision-making for continuous improvement in production systems. This analysis can be further extended to incorporate automation, predictive maintenance, and lean manufacturing techniques for long-term sustainability.