<p>Integrating solar energy into solar aircraft technology has become a significant technique to fulfill the rising energy demands of modern society. Utilizing solar energy as a fundamental heat source is extensively employed in sustainable lighting solutions such as photovoltaic panels, solar nanofluids, hybrid nanocomposites, increased oil recovery, and photovoltaic streetlights. Recent advancements focus on enhancing solar efficiency by combining solar energy with cutting-edge nanotechnology, notably in the aviation industry. The study focuses on the function of solar energy in aviation, with an emphasis on decreasing dependency on non-renewable energy sources, lowering carbon emissions, and enhancing overall efficiency. An essential element of the study is integrating Parabolic Trough Solar Collectors (PTSC) into aircraft systems to effectively harvest solar energy. Moreover, this study explores the Electrically Conducting Powell–Eyring Tetra-Hybrid Nanofluids (ECPETHNFs) in the context of enhanced solar aircraft wings. A theoretical model is applied to evaluate the flow, energy, and thermal behavior of PTSCs incorporated into a solar-powered aircraft, highlighting the significant role of nanoparticle’s thermal conductivity. The governing equations are simplified by utilizing suitable transformations and solved using numerical techniques. Additionally, an artificial intelligence-based approach, the Backpropagation Levenberg–Marquardt Scheme (BPLMS), and Response Surface Methodology (RSM) are employed to optimize the thermal and energy performance of the ECPETHNF model. A best-fit correlation is created using RSM to examine sensitivity rates. This examination explores heat transfer, entropy generation, and drag force, with results interpreted in terms of the magnetic number <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\((0.0\le M\le 2.0)\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">(</mo> <mn>0.0</mn> <mo>≤</mo> <mi>M</mi> <mo>≤</mo> <mn>2.0</mn> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation>, radiation parameter <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\((0.5\le Rd\le 1.5)\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">(</mo> <mn>0.5</mn> <mo>≤</mo> <mi>R</mi> <mi>d</mi> <mo>≤</mo> <mn>1.5</mn> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation>, and porosity parameter <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\((0.3\le {\lambda }^{*}\le 0.5)\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">(</mo> <mn>0.3</mn> <mo>≤</mo> <mmultiscripts> <mrow> <mi>λ</mi> </mrow> <mrow /> <mrow> <mrow /> <mo>∗</mo> </mrow> </mmultiscripts> <mo>≤</mo> <mn>0.5</mn> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation>. These findings are further analyzed using response RSM and BPLMS. Statistical analysis using analysis of variance (ANOVA) reveals high <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\({R}^{2}\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mi>R</mi> </mrow> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation> values of <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(97.96\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>97.96</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(95.99\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>95.99</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation>, and <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(96.99\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>96.99</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> for the response function heat transfer, entropy generation, and drag force related to <InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(M\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>M</mi> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq9"> <EquationSource Format="TEX">\({\lambda }^{*}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow> <mi>λ</mi> </mrow> <mrow /> <mrow> <mrow /> <mo>∗</mo> </mrow> </mmultiscripts> </math></EquationSource> </InlineEquation>, and <InlineEquation ID="IEq10"> <EquationSource Format="TEX">\(Rd\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="italic">Rd</mi> </mrow> </math></EquationSource> </InlineEquation>, respectively. These results indicate that the RSM model is highly effective for predicting these parameters. Additionally, the BPLMS model exhibits exceptional accuracy, with a Mean Squared Error (MSE) ranging between <InlineEquation ID="IEq11"> <EquationSource Format="TEX">\({E}^{-8}\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mi>E</mi> </mrow> <mrow> <mo>-</mo> <mn>8</mn> </mrow> </msup> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq12"> <EquationSource Format="TEX">\({E}^{-12}\)</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mi>E</mi> </mrow> <mrow> <mo>-</mo> <mn>12</mn> </mrow> </msup> </math></EquationSource> </InlineEquation>, positioning it as a robust alternative to RSM analysis. The results indicate that increasing the magnetic field, radiation intensity, and viscous dissipation significantly enhances heat transfer, while higher porosity reduces thermal efficiency. Among all tested fluids, the tetra-hybrid nanofluid achieved the best performance, exhibiting an approximately 82% improvement in heat transfer and about 19% reduction in drag compared with the base fluid. These findings confirm its suitability for high-efficiency solar thermal management in aerospace systems.</p>