This work investigates the feasibility of performing monocular depth estimation on highly resource-constrained hardware, specifically the Raspberry Pi Pico Zero microcontroller. In contrast to existing approaches that rely on large convolutional networks and high performance devices, this study explores a set of custom lightweight encoder-decoder architectures, including one inspired by L-ENet, L-EfficientUNet, μPyD-Net, and an LSTM-μPyD-Net combination, designed to operate within strict memory limits. These models were trained on a preprocessed KITTI dataset, with either LiDAR depth maps or SGM (Semi-Global Matching) dense depth maps, and evaluated in terms of accuracy, model size, and real-time inference performance. Results demonstrate that meaningful depth prediction is achievable on microcontrollers, paving the way for low-cost autonomous navigation systems and broader applications of TinyML in embedded robotics, with SGM proving to be the best preprocessing technique, and the LSTM-μPyD-Net having the best accuracy when trained on the full Train split of the KITTI dataset.