The ongoing opioid epidemic has made the need for alternative pain management strategies more urgent than ever. Nearly 1 in 5 Americans suffer from chronic pain, which has traditionally been treated with opioids and non-steroidal anti-inflammatory drugs (NSAIDs). However, both classes of drugs come with significant drawbacks. NSAIDs are often ineffective for managing chronic pain and can cause kidney and liver damage with prolonged use. Meanwhile, opioids lose their effectiveness over time, contributing to misuse, substance use disorders, and an increased risk of overdose. With few alternatives available that don't carry these risks, researchers are exploring new pain management options. One promising avenue is the use of cannabinoids, which are known for their anti-inflammatory and analgesic properties. In this study, I employ machine learning to create an unbiased kinematic and behavioral profile of mice experiencing chronic neuropathic pain using a custom-built linear track. Chronic pain and limb impairment are induced through partial sciatic nerve ligation, and a deep learning system analyzes videos of the mice to assess their movement patterns before and after treatment. I then compare these profiles to those of mice treated with NSAIDs, opioids, and cannabinoids, evaluating the effects of each treatment on behavioral measures like body position, which serves as a proxy for pain state and stress. We expect the mice treated with analgesics to show increased rearing and grooming behaviors. This research not only compares the analgesic effectiveness of cannabinoids to traditional pain-relief drugs but also helps reduce the stigma surrounding cannabinoid-based treatments.