Background <p>Wireless Body Area Networks (WBANs) are essential for continuous health monitoring but face critical challenges due to dynamic link instability caused by body movements and strict bio-safety requirements. The fluctuating wireless channel leads to high packet loss, while radio frequency (RF) energy absorption, measured as Specific Absorption Rate (SAR), must remain below 1.6 W/kg to prevent tissue damage. Existing routing protocols often react to failures rather than predict them and rarely co-optimize reliability with thermal safety.</p> Methods <p>This paper proposes PACO-CLS (Predictive Ant Colony Optimization with Correlated Link Stability), a novel routing protocol that proactively addresses both network dynamics and bio-safety. It leverages tri-axial accelerometer data from sensor nodes to classify body posture using a Gaussian Mixture Model (GMM). This posture state conditions a Vector Autoregressive (VAR) model, which predicts future link quality metrics (SNR, distance, SAR) over a 0.5-second horizon. These predictions drive an enhanced Ant Colony Optimization (ACO) algorithm, where path selection is guided by a multi-objective cost function. Crucially, the pheromone evaporation rate is dynamically modulated by the cumulative SAR, calculated via the Pennes bio-heat equation, to enforce thermal safety.</p> Results <p>Extensive simulations in NS-3.40, calibrated with empirical data from the MySignals HW V3 platform, demonstrate that PACO-CLS significantly outperforms QC-TriL, LEACH-WBAN, and Q-Learning-based routing. It achieves a network lifetime of <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(32.4 \pm 1.2\)</EquationSource></InlineEquation> hours (a 40.3% improvement), an energy efficiency of <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(17.8 \pm 0.2\)</EquationSource></InlineEquation> Mb/J, a packet delivery ratio of <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(98.5\% \pm 0.3\%\)</EquationSource></InlineEquation>, and end-to-end latency of <InlineEquation ID="IEq4"><EquationSource Format="TEX">\(36.2 \pm 2.1\)</EquationSource></InlineEquation> ms. Critically, it maintains an average SAR of <InlineEquation ID="IEq5"><EquationSource Format="TEX">\(0.82 \pm 0.11\)</EquationSource></InlineEquation> W/kg, well below the safety threshold across all tested scenarios, including high interference (<InlineEquation ID="IEq6"><EquationSource Format="TEX">\(I_d=0.3\)</EquationSource></InlineEquation> sources/m²) and mobility (<InlineEquation ID="IEq7"><EquationSource Format="TEX">\(v=1.5\)</EquationSource></InlineEquation> m/s).</p> Conclusions <p>By integrating predictive analytics with thermal-aware mechanisms, PACO-CLS establishes a new benchmark for intelligent WBAN routing. Its ability to proactively avoid unstable links based on correlated motion analytics and simultaneously enforce bio-safety constraints makes it a robust and clinically viable solution. PACO-CLS consistently meets stringent clinical requirements for real-time monitoring (PDR <InlineEquation ID="IEq8"><EquationSource Format="TEX">\( &gt; \)</EquationSource></InlineEquation> 95%, <InlineEquation ID="IEq9"><EquationSource Format="TEX">\(D_{e2e}\)</EquationSource></InlineEquation><InlineEquation ID="IEq10"><EquationSource Format="TEX">\( &lt; 50\)</EquationSource></InlineEquation> ms, SAR <InlineEquation ID="IEq11"><EquationSource Format="TEX">\( &lt; 1\)</EquationSource></InlineEquation> W/kg, <InlineEquation ID="IEq12"><EquationSource Format="TEX">\(T_{\text{life}} &gt; 24\)</EquationSource></InlineEquation> h), demonstrating its potential for next-generation, safe, and reliable healthcare applications.</p>