Heart rate variability (HRV) is an essential physiological marker widely used to assess cardiovascular regulation and autonomic nervous system activity, reflecting complex, nonlinear dynamics in biological control mechanisms. HRV signals demonstrate long-range correlations, scale-invariant properties, and memory effects that challenge traditional integer-order modeling approaches. Fractal geometry and fractional calculus have emerged as powerful frameworks for analyzing such physiological signals, enabling a deeper understanding of temporal and structural complexity. This work explores the application of fractal measures and fractional-order modeling techniques to HRV analysis, emphasizing their ability to capture multiscale structures, self-similarity, and non-local temporal dynamics. Various fractal descriptors and fractional formulations reported in the literature are conceptually compared in terms of their biological interpretability, analytical performance, and potential application under different physiological or pathological conditions. Special attention is given to how non-integer order operators enhance the representation of physiological variability, anomalous fluctuations, and adaptive responses in cardiac rhythms. Furthermore, the discussion includes potential clinical and research implications, highlighting how these approaches may contribute to improved monitoring, diagnosis, and understanding of cardiovascular health. The synthesis highlights the methodological trends, advantages, and current limitations of fractal and fractional approaches, providing a comprehensive perspective on the study of complex biological rhythms. Overall, this work supports the integration of advanced mathematical tools in quantitative biology and underscores their potential for improving understanding and monitoring of cardiovascular dynamics.