Heart rate variability (HRV) reflects the physiological factors influencing heart rhythm and provides insights into the interaction between the sympathetic and parasympathetic nervous systems. HRV is a nonstationary signal that can indicate current or impending cardiac issues. Analyzing HRV, particularly through instantaneous HR against time, is a noninvasive method for assessing autonomic nervous system (ANS) activity. Computer-based tools enable in-depth analysis of HRV over long periods, aiding in diagnostics. This review discusses various applications of HRV and analytical techniques, including linear, frequency domain, wavelet domain, and nonlinear methods. HRV is a valuable indicator of ANS status and cardiac health. It reflects the heart's ability to adapt to changes and is influenced by the balance between the sympathetic and parasympathetic nervous systems. Recent studies show that HRV can predict sudden cardiac death risk in postinfarction and diabetic patients. HRV measurements are noninvasive, reproducible, and easy to perform under standardized conditions. HRV analysis has been used to detect early signs of neonatal sepsis and systemic inflammatory response syndrome (SIRS). Binary symbolization of RR intervals has revealed important insights into heart period regularity. Wavelet analysis has shown promise in evaluating HRV oscillations and identifying patterns during reperfusion. It also helps in diagnosing obstructive sleep apnoea syndrome by analyzing time-frequency parameters. HRV analysis using wavelet transforms provides useful information for assessing dynamic changes in HRV during myocardial ischaemia. The study highlights the importance of HRV in understanding cardiovascular health and the role of the ANS in regulating heart rate. Various analytical techniques, including linear and nonlinear methods, are used to analyze HRV signals, offering valuable insights into cardiac function and disease risk.Heart rate variability (HRV) reflects the physiological factors influencing heart rhythm and provides insights into the interaction between the sympathetic and parasympathetic nervous systems. HRV is a nonstationary signal that can indicate current or impending cardiac issues. Analyzing HRV, particularly through instantaneous HR against time, is a noninvasive method for assessing autonomic nervous system (ANS) activity. Computer-based tools enable in-depth analysis of HRV over long periods, aiding in diagnostics. This review discusses various applications of HRV and analytical techniques, including linear, frequency domain, wavelet domain, and nonlinear methods. HRV is a valuable indicator of ANS status and cardiac health. It reflects the heart's ability to adapt to changes and is influenced by the balance between the sympathetic and parasympathetic nervous systems. Recent studies show that HRV can predict sudden cardiac death risk in postinfarction and diabetic patients. HRV measurements are noninvasive, reproducible, and easy to perform under standardized conditions. HRV analysis has been used to detect early signs of neonatal sepsis and systemic inflammatory response syndrome (SIRS). Binary symbolization of RR intervals has revealed important insights into heart period regularity. Wavelet analysis has shown promise in evaluating HRV oscillations and identifying patterns during reperfusion. It also helps in diagnosing obstructive sleep apnoea syndrome by analyzing time-frequency parameters. HRV analysis using wavelet transforms provides useful information for assessing dynamic changes in HRV during myocardial ischaemia. The study highlights the importance of HRV in understanding cardiovascular health and the role of the ANS in regulating heart rate. Various analytical techniques, including linear and nonlinear methods, are used to analyze HRV signals, offering valuable insights into cardiac function and disease risk.