Gamma Knife radiosurgery (GKRS) is a well-established radiation therapy (RT) technique for treating brain tumors. However, the planning process for GKRS is complex and time-consuming, heavily reliant on the expertise of medical physicists. Incorporating deep learning approaches for GKRS dose prediction can reduce this dependency, improving planning efficiency and homogeneity, streamlining clinical workflows, and reducing patient lagging times. Despite this, precise Gamma Knife plan dose distribution prediction using existing models remains a significant challenge. The complexity stems from the intricate nature of dose distributions, subtle contrasts in CT scans, and the interdependence of dosimetric metrics. In an effort to overcome these challenges, we have developed a "Cascaded-Deep-Supervised" Convolutional Neural Network (CDS-CNN) that employs a hybrid-weighted optimization scheme. Our innovative method incorporates multi-level deep supervision along with a strategic sequential multi-network training approach. It enables the extraction of intra-slice and inter-slice features, leading to more realistic dose predictions with additional contextual information. CDS-CNN was trained and evaluated using data from 105 brain cancer patients who underwent GKRS treatment, with 85 cases used for training and 20 for testing. Quantitative evaluations and statistical analyses demonstrated high consistency between the predicted dose distributions and the reference doses from the treatment planning system (TPS). The 3D overall gamma passing rates (GPRs) reached 97.15%±1.36% (3mm/3%, 10% threshold). When evaluated using the more stringent criteria of 2mm/3%, 10% threshold, the overall GPRs still achieved 96.33%±1.08%. Furthermore, the average target coverage (TC) was 98.33%±1.16%, dose selectivity (DS) was 0.57±0.10, gradient index (GI) was 2.69±0.30, and homogeneity index (HI) was 1.79±0.09. The experimental results showed that the proposed CDS-CNN outperformed other models in predicting GKRS dose distributions, with the prediction being the closest to the TPS dose.