Digital image segmentation is essential in image processing, influencing the accuracy of higher-level tasks. Thresholding is widely used, yet identifying optimal threshold values remains challenging. The Firefly Algorithm with Neighbourhood Attraction (FaNA), a metaheuristic approach, is efficient for color image thresholding but underperforms on grayscale images due to suboptimal thresholds. To overcome this, an enhanced version (eFaNA) was developed by integrating a chaotic tent map for population initialization and a Lévy flight-based random walk for improved exploration. eFaNA was compared with FaNA, fuzzy firefly algorithm (FFA), and the standard Firefly Algorithm (FA) in multilevel thresholding of grayscale images. Results demonstrate that eFaNA achieves superior segmentation quality with minimal detail loss, outperforming the others. The average PSNR obtained by eFaNA, FFA, FaNA, and FA was 25.5320 dB, 25.4075 dB, 24.1522 dB, and 24.4506 dB, respectively; average SSIM was 0.8641, 0.8604, 0.8432, and 0.6703; and execution time was 50.5322, 38.7726, 38.7528, and 107.6340 seconds, respectively. This reflects a PSNR improvement of 5.71% over FaNA, 0.49% over FFA, and 4.42% over FA, and an SSIM gain of 2.48% over FaNA, 0.43% over FFA, and 28.92% over FA. While eFaNA lags behind FFA and FaNA in execution time by ~11.8 seconds, it significantly outperforms FA. The performance gain is attributed to the chaotic tent map’s diverse initialization and the Lévy flight’s enhanced search capability. These improvements enable eFaNA to deliver consistently better threshold values and segmentation results. However, its relatively higher computational cost may limit applicability in real-time image processing.