Heart rate variability (HRV) is a reliable indicator of the physiological factors that modulate the heart's normal rhythm, reflecting the interplay between the sympathetic and parasympathetic nervous systems. HRV analysis is a powerful tool for assessing cardiac health and the autonomic nervous system (ANS). The paper discusses various applications of HRV and the techniques used for its analysis, including linear, frequency domain, wavelet domain, and nonlinear methods. HRV is particularly useful for diagnosing and monitoring conditions such as post-infarction and diabetic patients, as well as for understanding the cardiorespiratory control system. Recent studies have explored new dynamic methods like Lyapunov exponents, 1/f slope, approximate entropy, and detrended fluctuation analysis to uncover nonlinear fluctuations in HRV. Wavelet transform has been shown to provide valuable insights into time-dependent spectral analysis and dynamic changes in HRV during myocardial ischemia and obstructive sleep apnea syndrome. The paper also highlights the impact of various factors on HRV, such as ANS activity, blood pressure, myocardial infarction, age, gender, and lifestyle factors.Heart rate variability (HRV) is a reliable indicator of the physiological factors that modulate the heart's normal rhythm, reflecting the interplay between the sympathetic and parasympathetic nervous systems. HRV analysis is a powerful tool for assessing cardiac health and the autonomic nervous system (ANS). The paper discusses various applications of HRV and the techniques used for its analysis, including linear, frequency domain, wavelet domain, and nonlinear methods. HRV is particularly useful for diagnosing and monitoring conditions such as post-infarction and diabetic patients, as well as for understanding the cardiorespiratory control system. Recent studies have explored new dynamic methods like Lyapunov exponents, 1/f slope, approximate entropy, and detrended fluctuation analysis to uncover nonlinear fluctuations in HRV. Wavelet transform has been shown to provide valuable insights into time-dependent spectral analysis and dynamic changes in HRV during myocardial ischemia and obstructive sleep apnea syndrome. The paper also highlights the impact of various factors on HRV, such as ANS activity, blood pressure, myocardial infarction, age, gender, and lifestyle factors.