Abstract Demonstration of quantum advantage for classical machine learning tasks remains a central goal for quantum technologies and artificial intelligence. Two major bottlenecks to this goal are the high dimensionality of practical datasets and the limited performance of near-term quantum computers. Boson sampling is among the few models for which experiments have claimed quantum advantage, yet it has limited practical applications. Here, we propose a hybrid framework that combines the computational power of boson sampling with the adaptability of neural networks to construct quantum kernels that enhance support vector machine classification. The neural network adapts the data features onto a programmable boson sampling circuit, producing quantum states that span a high-dimensional Hilbert space and enable improved classification performance. Using four datasets with various classes, we demonstrate that our model outperforms classical linear and sigmoid kernels. These results highlight the potential of boson sampling-based quantum kernels for practical quantum-enhanced machine learning. Subjects Acknowledgements AB acknowledges support from the National Natural Science Foundation of China (grants No. W2541020, No. 12274059, No. 12574528, and No. 1251101297). The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. The authors would like to thank S. Sarkar and C. Mukhopadhyay for useful discussions. Ethics declarations Competing interests Author Abolfazl Bayat is Associate Editor of npj Quantum Information. Abolfazl Bayat was not involved in the journal’s review of, or decisions related to, this manuscript. The other authors do not have a competing interest. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you