Resistive sensors are widely employed in industrial, environmental, and biomedical systems, yet the efficiency and accuracy of the readout circuitry fundamentally constrain their overall performance. Existing literature exhibits fragmented coverage of readout architectures, inconsistent performance metrics, and a lack of a unified framework for comparing voltage-, frequency-, and time-domain conversion techniques. These limitations hinder the assessment of trade-offs in power consumption, resolution, dynamic range, and scalability, factors critical to modern Internet of Things (IoT) and wearable applications. This review addresses these issues through a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic survey, complemented by a decade-long bibliometric analysis (2015–2025). A unified performance evaluation framework is proposed, standardizing key metrics such as linearity, resolution, noise immunity, temperature stability, and energy efficiency. The three principal resistance-to-digital pathways, analog-to-digital converter (ADC)-based, frequency-based, and time-based architectures, are examined in detail, highlighting their operating principles, circuit innovations, and design trade-offs. Furthermore, consolidated comparison tables and architecture-independent figures of merit (FoMs), including a generalized FoM, enable consistent benchmarking across converter types. The analysis indicates that time-based interfaces offer the most balanced combination of energy efficiency and scalability, frequency-based designs excel in wide resistance ranges, and ADC-based approaches remain preferred for high-precision, narrow-range sensing. Overall, this review provides a coherent foundation for selecting suitable readout architectures and identifies key opportunities for advancing low-power, high-accuracy resistive sensor interface technologies.