Mangroves are indispensable in the coastal ecosystem and are depleting due to man-made and natural causes. Attempts are being made to mitigate the impact on mangroves, and transplantation is considered a strategy for mangrove regeneration. However, it is essential to identify suitable regions and select mangrove species compatible with these regions to achieve maximum benefit from transplantation. Mangrove transplantation is increasingly adopted as a restoration strategy, yet its success hinges on selecting species adapted to site-specific environmental conditions. This study utilized an unsupervised machine learning (ML) approach, a Self-Organizing Map (SOM) model, to predict mangrove species distribution using readily available data, requiring minimal labor, time, and cost. The data used in this study were gathered from the literature and included climatic, hydrological, and solar insolation variables. An SOM model with an 11 × 8 neuron grid was developed to cluster species based on their ecological preferences. Model accuracy was assessed through Quantization error (0.0003) and Topographic error (0.0548), confirming strong predictive performance and topological preservation. The SOM model effectively predicts mangrove species across climatic zones, capturing their zonal preferences based on input variables. This approach offers a practical decision-support tool for restoration practitioners, improving site–species matching and thereby increasing transplantation success rates. Future refinement could be achieved by integrating additional environmental parameters such as soil salinity, tidal dynamics, and seasonal microclimatic variations to better capture temporal shifts in species distribution. Hence, findings highlight the potential of ML to guide cost-effective, evidence-based mangrove conservation and ecological restoration strategies.