Abstract: To address the issues of insufficient feature extraction and inadequate context modeling in monocular depth estimation, a multi-scale feature fusion optimization framework, FCMdepth, was proposed to enhance prediction performance. FCMdepth adopts an encoder-decoder structure, where the encoder, FC-Net, consists of MobileNetV3-F and CDBlock, and features were optimized through multi-scale extraction and dilated convolutions. The decoder, LapMA-Net, combines the Laplacian pyramid with an Efficient Multi-scale Attention (EMA) module to enhance cross-scale feature fusion and outputs accurate depth maps. Experiments on the KITTI and NYU-Depth V2 datasets show that FCMdepth outperforms models such as Lite-mono, Hr-depth, and Lapdepth in four error metrics: Root Mean Square Error (RMSE), Root Mean Square Logarithmic Error (RMSE_Log), Absolute Relative error (Abs_Rel), and Square Relative error (Sq_Rel), with average reductions of 0.605, 0.117, 0.183, and 0.279, respectively. Furthermore, FCMdepth framework improves three accuracy metrics by an average of 1.5, 1.4, and 0.8 percentage points. FCMdepth demonstrates superior performance compared to other methods and provides an effective reference for monocular depth estimation and 3D reconstruction in complex scenes.