Abstract
Railways remain an essential part of modern transportation, yet their safe functioning is often determined by the actual condition of the tracks. The study looks at various ways to detect faults in the rail infrastructure and splits them broadly into two categories: static and dynamic techniques. Different countries rely on different tools to monitor tracks, and this paper compares those tools based on practical factors like how precise they are, how much ground they cover, and how difficult or costly they are to operate. Rather than simply listing pros and cons, we try to show where each method works best. To make sense of the data, visuals like charts and summaries were added, making it easier to see where each approach fits. One part of the analysis pays special attention to how certain features of the railway - such as how wide the rails are or how much the outer rail is elevated - can influence the choice of inspection methods. Lately, there's been a shift toward smarter diagnostics. Technologies like AI, digital simulations of tracks (known as digital twins), and systems using many sensors at once are gaining ground. These tools are changing how track inspections are done and offer new opportunities for early problem detection. This paper doesn't just list methods - it gives a clear structure for understanding which approach fits what context. The outcomes can help transportation teams fine-tune how they take care of tracks and make the system more dependable in the long term.