Gait analysis finds applications in various domains, including medical diagnostics, human identification, and sports biomechanics. The most popular method for recording gait is the passive marker system, such as VICON and Reflex markers. These marker devices are costly, and placing markers on a subject’s body can be time-consuming. Furthermore, setting up markers for some rehabilitation patients, like those who have had a stroke or spinal cord injury, might be challenging. In response to these limitations, this research introduced a cost-effective gait analysis method using an efficient MediaPipe pose model and a standalone phone camera. Human gait parameter, especially joint angles, is also calculated to analyze gait across different attire styles, namely formal, suit, and saree. Understanding how attire styles influence human gait is crucial not only for rehabilitation purposes but also for medical applications and gait recognition. A statistical analysis is performed to compare gait between attire styles, and mean error values for knee, hip, and ankle angles are computed for each attire style: saree (8.47, 6.41, 9.66), suit (6.47, 3.13, 5.36), and formal (0.60, 0.62, 5.29), respectively. Our findings reveal that the measured angle error is less than 7 degrees for formal and suit attire, indicating that gait identification is more accurate in these outfits compared to a saree. These results show that wearing different types of clothing affects how people walk, leading to noticeable changes in gait parameters like joint angles. These changes can make it harder to recognize someone’s gait accurately and may also complicate precise gait analysis for medical purposes. By understanding how different attire styles affect gait, future biometric systems can incorporate these findings to enhance accuracy.