The increasing demand for compact and energy-efficient heat exchangers has accelerated the development of passive heat transfer enhancement techniques and intelligent predictive tools for improved thermal system design. In the present study, an experimental and machine learning-based investigation was conducted to evaluate the thermo-hydraulic performance of a counter-flow double-pipe heat exchanger equipped with helical wire coil (WC) inserts under turbulent flow conditions. The novelty of this work lies in the systematic assessment of fifteen insert configurations by combining three wire diameters (1.0, 1.5, and 2.0 mm) with five pitch ratios (P/Dc = 0.625, 1.25, 1.875, 2.5, and 3.125). Experiments were performed over a Reynolds number range of 5500-15000, and the experimental setup was validated against established Nusselt number and friction factor correlations. The thermo-hydraulic performance was evaluated using the heat transfer coefficient, Nusselt number, friction factor, pressure drop, and thermal performance factor. The results showed that the wire coil inserts generated strong swirl flow and secondary vortices, which enhanced fluid mixing and suppressed thermal boundary layer development, resulting in a maximum Nusselt number enhancement of 126.7% compared with the plain tube. The highest Nusselt number (180.24) was achieved using a 2 mm wire diameter with a P/Dc of 0.625. Although the friction factor increased by 156.5-410.8% due to greater flow resistance, the thermal performance factor remained above unity (1.01-1.35) for all configurations, confirming the overall thermo-hydraulic effectiveness of the inserts. Furthermore, Linear Regression, Ridge Regression, Random Forest, and Gradient Boosting models were developed to predict the heat transfer and flow characteristics. Among them, the Gradient Boosting model demonstrated the highest prediction accuracy, achieving R2 values of 0.9984 and 0.9971 for the Nusselt number and friction factor, respectively, highlighting the potential of machine learning for the rapid design and optimization of advanced heat exchanger systems.