This paper presents a numerical investigation into the thermal performance of helical coils, assessing the impact of spacing, wire diameter ratio, and coil pitch ratio on the pressure drop and heat transfer characteristics. Heat flux was maintained at 1000 W/m2, while Reynolds numbers ranged from 5000 to 30,000. Coil configurations encompassed pitch ratios within 1 ≤ p/d ≤ 3, wire diameter ratios spanning 0.044 ≤ e/d ≤ 0.133, and spacing intervals of 0.5, 1, and 2 mm. The finite volume method was employed to solve the energy, Navier-Stokes, and 3D continuity equations. Results indicate substantial influences of wire diameter, coil pitch ratios, and spacing on Nusselt number friction factors within the coiled tube. Notably, Nusselt number enhancements within the studied intervals ranged from 16 % to 102 %, with an overall performance criteria improvement reaching 43 %. Furthermore, a machine learning approach utilizing artificial neural networks (ANN) was employed, with comparisons drawn between results obtained from the ANN and computational fluid dynamics (CFD). The congruence between experimental, CFD, and ANN findings underscores the reliability of the study outcomes.