LiDAR and cameras play a vital role in autonomous vehicles by providing complementary data for object detection and environmental perception. However, achieving seamless data integration from these sensors depends on partial and temporal synchronization. Unlike conventional methods that depend on pre-calibrated datasets, our methodology utilizes a custom-acquired multimodal dataset comprising both image and video data from a monocular camera and point cloud data from a VLP-16 Velodyne LiDAR sensor. In this paper, we proposed a comprehensive framework for LiDAR and camera calibration and temporal synchronization of real time data, synthesized and validated in a controlled lab environment. Calibration of the raw data was performed using a checkerboard as the target to ensure accurate spatial alignment between heterogeneous sensor systems.The collected corpus is further timestamped, synchronized, and validated.The accuracy of the proposed methodology is evaluated by projecting LiDAR points onto image frames, enabling qualitative verification of spatial and temporal consistency. The proposed method integrates target-based calibration with software-level timestamp synchronization to create a reproducible and scalable calibration pipeline. Results demonstrate accurate alignment across modalities, validating the effectiveness of our approach. This work provides a practical contribution to multi-sensor fusion research, especially for applications requiring custom datasets or operating in constrained environments.