Despite the mathematical excellence of CHSH-based QKD, their engineering possibilities compromise their security foundations, creating space for the so-called Quantum Hacking. Several real-world quantum hacking techniques have been successfully demonstrated against commercial QKD systems in 2025, utilising methods such as Phase Remapping Attack, Trojan Horse Attack, Time-Shift Attack, and Detector Blinding Attack. This work introduces a new method to exploit weak Random Number Generators (RNGs) in QKD systems using quantum RNG (qRNG) and machine learning. We demonstrate how compromised RNGs enable side-channel attacks by monitoring entropy and external factors (e.g., temperature). Our framework integrates: Experimental results show CHSH scores correlate with RNG quality (Rigetti: 0.8036, IonQ: 0.8362), while gate fidelity (IonQ: 99.4% vs. Rigetti: 93.6%) impacts certifiable randomness. Combining device-independent CHSH validation with machine learning, this framework detects attacks such as phase remapping and detector blinding through entropy deviations.