Predicting Carbon dioxide (CO₂) emissions is essential for guiding environmental policies and climate strategies, yet accurate local-scale prediction remains challenging due to complex spatial and temporal factors. Existing approaches often focus on regional or global models and do not fully exploit high-resolution satellite imagery for local trend analysis. To overcome these limitations, Conditional Pine Cone Optimization enabled Attention-Based Long Short-Term Memory (CPCO_ALSTM) is devised for CO2 Emission prediction for Trend Analysis in Satellite Image. In this approach, satellite images of the query region acquired at different time intervals are used as input. Initially, a Kalman Filter is employed for noise reduction and temporal smoothing during preprocessing. Then, segmentation of satellite image is done using Dense-Res Recurrent Prototypical Network (DRRP-Net), wherein DRRP-Net is developed by combining Dense-Res-Inception Network (DRINet) and Recurrent Prototypical Network (RP-Net). Based on the segmented regions and extracted features, carbon emission estimation is carried out. The estimated CO₂ emissions are then validated against ground-truth emission data obtained from a reference database to assess prediction accuracy. For temporal trend analysis, an Attention-Based Long Short-Term Memory (ALSTM) model is utilized to capture long-range dependencies in emission patterns. The ALSTM parameters are optimally tuned using the proposed CPCO algorithm, which combines Pine Cone Optimization (PCO) with Conditional Autoregressive Value at Risk (CAViaR) to enhance convergence stability and prediction robustness. Additionally, CPCO_ALSTM has attained 96.35% of accuracy, 0.087 of normalized MSE, and 0.957 of Jaccard Coefficient.