This paper presents a novel deep learning framework for robotic path planning that seamlessly integrates Linear Temporal Logic (LTL) with trajectory optimization to meet mission specifications efficiently. Our approach innovates on several fronts: First, by training a neural network end-to-end to generate control sequences that are not only cost-effective but also fully compliant with LTL-defined mission objectives. This negates the need for generating traditional automatons, as our network is capable of directly interpreting LTL formulas to guide path planning. Key to our framework is the use of a Conditional Variational Autoencoder (CVAE), which is adept at identifying the optimal distribution of trajectories. This enables the extraction of practical control sequences through a process of sampling latent variables and inferring control outputs, thus addressing the critical challenge of trajectory optimization under uncertainty. Moreover, our model incorporates transformer networks to refine these trajectory distributions into nearly optimal control sequences, further enhanced by a Gaussian Mixture Model (GMM) to manage uncertainty and fine-tune adjustments effectively. Empirical validation through comparative simulations showcases the superior performance of our model. It achieves significant advancements in trajectory optimality and mission success rates over existing deep learning-based path planning strategies. This work underscores the potential of integrating LTL in deep learning models for robotic path planning, marking a significant leap forward in the domain.